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Workshops



Instructions for Workshop papers


Paper Submission

Workshop papers must be submitted using the GECCO submission site. After login, the authors need to select the "Workshop Paper" submission form. In the form, the authors must select the workshop they are submitting to. To see a sample of the "Workshop Paper" submission form go to GECCO's submission site and chose "Sample Submission Forms".

Submitted papers must not exceed 8 pages (excluding references) and are required to be in compliance with the GECCO 2020 Papers Submission Instructions. It is recommended to use the same templates as the papers submitted to the main tracks. It is not required to remove the author information if the workshop the paper is submitted to does not have a double-blind review process (please, check the workshop description or the workshop organizers on this).

All accepted papers will be presented at the corresponding workshop and appear in the GECCO Conference Companion Proceedings. By submitting a paper, the author(s) agree that, if their paper is accepted, they will:

  • Submit a final, revised, camera-ready version to the publisher on or before the camera-ready deadline
  • Register at least one author before April 27, 2020 to attend the conference
  • Attend the conference (at least one author)
  • Present the accepted paper at the conference


Important Dates

These dates are strict, no extensions will be granted

  • Submission opening: February 27, 2020
  • Submission deadline: April 3, 2020
  • Notification of acceptance: April 17, 2020
  • Camera-Ready Material: April 24, 2020
  • Author registration deadline: April 27, 2020


Each paper accepted needs to have at least one author registered before the author registration deadline. If an author is presenting more than one paper at the conference, she/he does not pay any additional registration fees.

List of Workshops

TitleOrganizers
ADRR — Automated Design of Robots for the Real-world
  • David Howard CSIRO
  • Emma Hart Edinburgh Napier University
  • Gusz Eiben VU Amsterdam
BENCHMARK — Good Benchmarking Practices for Evolutionary Computation
  • Tome Eftimov Stanford University / Jožef Stefan Institute
  • William La Cava University of Pennsylvania
  • Boris Naujoks Cologne University of Applied Sciences, Germany
  • Pietro Oliveto The University of Sheffield
  • Vanessa Volz modl.ai
  • Thomas Weise Hefei University, China
DTEO — Workshop on Decomposition Techniques in Evolutionary Optimization
  • Bilel Derbel University of Lille
  • Ke Li University of Exeter, UK
  • Xiaodong Li RMIT, Australia
  • Saúl Zapotecas Autonomous Metropolitan University-Cuajimalpa, México
  • Qingfu Zhang City University of Hong-Kong
E-MaOP — Evolutionary Many-objective Optimization
  • Rui Wang National University of defense technology, China.
  • Ran Cheng Southern University of Science and Technology
  • Guohua Wu Central South University
  • Miqing Li School of Computer Science, University of Birmingham, United Kingdom
  • Hisao Ishibuchi Southern University of Science and Technology
EAPwU — Evolutionary Algorithms for Problems with Uncertainty
  • Jonathan Fieldsend University of Exeter, UK
  • Ozgur Akman University of Exeter
  • Khulood Alyahya University of Exeter
  • Jürgen Branke Warwick Business School
EC+MCDM — Workshop on Evolutionary Computation + Multiple Criteria Decision Making (EC + MCDM)
  • Tinkle Chugh University of Exeter
  • Richard Allmendinger The University of Manchester, UK
  • Jussi Hakanen University of Jyväskylä
ECADA — 10th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA)
  • John R. Woodward QUEEN MARY, UNIVERSITY OF LONDON
  • Daniel R. Tauritz Auburn University
  • Emma Hart Edinburgh Napier University
ECPERM — Evolutionary Computation for Permutation Problems
  • Marco Baioletti University of Perugia
  • Josu Ceberio Uribe University of the Basque Country (UPV/EHU)
  • John McCall
  • Alfredo Milani University of Perugia, Italy
EvoSoft — Evolutionary Computation Software Systems
  • Stefan Wagner University of Applied Sciences Upper Austria
  • Michael Affenzeller University of Applied Sciences Upper, Austria
GI@GECCO — The Ninth Genetic Improvement Workshop (2020)
  • Bradley Alexander University of Adelaide
  • Alexander (Sandy) Brownlee University of Stirling
  • Saemundur O. Haraldsson University of Stirling
  • Markus Wagner University of Adelaide
  • John R. Woodward QUEEN MARY, UNIVERSITY OF LONDON
GreenAI — Green AI: Evolutionary and machine learning solutions in environment, renewable and ecologically-aware scenarios
  • Nayat Sánchez-Pi Rio de Janeiro State University
  • Luis Martí Inria Chile
IAM 2020 — 5th Workshop on Industrial Applications of Metaheuristics
  • Silvino Fernandez Alzueta ArcelorMittal
  • Pablo Valledor Pellicer ArcelorMittal
  • Thomas Stützle IRIDIA laboratory, ULB, Belgium
iGECCO — Interactive Methods @ GECCO
  • Matthew Johns University of Exeter
  • Nick Ross University of Exeter
  • Ed Keedwell University of Exeter
  • Herman Mahmoud University of Exeter
  • David Walker University of Plymouth
IWLCS 2020 — 23rd International Workshop on Learning Classifier Systems
  • Anthony Stein University of Augsburg, Germany
  • Masaya Nakata The University of Electro-Communications, Japan
  • David Pätzel Institute of Computer Science, University of Augsburg
MedGEC — Medical Applications of Genetic and Evolutionary Computation
  • Neil Vaughan University of Chester
  • Stephen Smith University of York, UK
  • Stefano Cagnoni Universita' degli Studi di Parma, Italy
  • Robert M. Patton Oak Ridge National Laboratory, USA
NEvo@Work — Neuroevolution at work
  • Ivanoe De Falco National Research Council of Italy (CNR) - Institute of High-Performance Computing and Networking (ICAR)
  • Antonio Della Cioppa Natural Computation Lab, DIEM, University of Salerno
  • Umberto Scafuri National Research Council of Italy (CNR) - Institute of High-Performance Computing and Networking (ICAR)
  • Ernesto Tarantino National Research Council of Italy (CNR) - Institute of High-Performance Computing and Networking (ICAR)
PDEIM — Parallel and Distributed Evolutionary Inspired Methods
  • Ernesto Tarantino National Research Council of Italy (CNR) - Institute of High-Performance Computing and Networking (ICAR)
  • Ivanoe De Falco National Research Council of Italy (CNR) - Institute of High-Performance Computing and Networking (ICAR)
  • Antonio Della Cioppa Natural Computation Lab, DIEM, University of Salerno
  • Umberto Scafuri National Research Council of Italy (CNR) - Institute of High-Performance Computing and Networking (ICAR)
RWACMO — Real-World Applications of Continuous and Mixed-Integer Optimization
  • Akira Oyama Japan Aerospace Exploration Agency
  • Koji Shimoyama Tohoku University, Japan
  • Hemant Kumar Singh University of New South Wales, Australia
  • Kazuhisa Chiba University of Electro-Communications, Japan
  • Pramudita Satria Palar Bandung Institute of Technology, Indonesia
SAEOpt — Workshop on Surrogate-Assisted Evolutionary Optimisation
  • Alma Rahat Swansea University, UK
  • Richard Everson University of Exeter, UK
  • Jonathan Fieldsend University of Exeter, UK
  • Handing Wang University of Surrey
  • Yaochu Jin University of Surrey
SecDef 2020 — Genetic and Evolutionary Computation in Defense, Security, and Risk Management
  • Erik Hemberg MIT CSAIL, ALFA
  • Riyad Alshammari King Saud bin Abdulaziz University for Health Sciences
  • Tokunbo Makanju Cybersecurity Laboratory at KDDI Research, Fuijimino-shi
SWINGA — Swarm Intelligence Algorithms: Foundations, Perspectives and Challenges
  • Ivan Zelinka Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VŠB-TUO, Ostrava-Poruba, Czech Republic & IT4Innovations, National Supercomputing Centre, senior researcher, Big Data Analysis Lab www.it4i.cz
  • Swagatam Das ISI, Kolkatta, India
  • Ponnuthurai Nagaratnam Suganthan Nanyang Technological University
  • Roman Senkerik Tomas Bata University in Zlin
VizGEC 2020 — Visualisation Methods in Genetic and Evolutionary Computation
  • David Walker University of Plymouth
  • Richard Everson University of Exeter, UK
  • Rui Wang Research Scientist
  • Neil Vaughan University of Chester

ADRR — Automated Design of Robots for the Real-world

http://tiny.cc/t9x1jz

Summary

Evolution offers great promise for the automatic design of bespoke robots whose form and function are specifically tailored to meet the demands of given tasks and environments. However, real-world deployments of evolved robots have (with a few notable exceptions) been relatively sparse.

Motivated by the possibilities afforded by new-found, easily accessible advanced manufacturing processes including 3D printing, and the future promise of bespoke ‘robotic materials’, we invite interested participants to join us in a robust discussion of the current state of the field as we sketch a roadmap towards the mainstream adoption of evolutionary paradigms into robotic design processes. We encourage a specific focus on real-world applications. We will hear from invited world-leading experts who will share their success stories and cautionary tales.

As well as a panel discussion and 'show and tell' session, we are seeking contributions for a poster session in the following areas:
• Coevolution of behaviour and control
• Learning in hardware with limited / noisy data
• Integration of evolutionary approaches with traditional design techniques
• Environmentally-mediated behaviours
• Useful levels of abstraction in evolutionary processes
• Building and managing complexity (modularity, hierarchy, indirect encodings, etc).
• Integration of expert knowledge and requirements into evolutionary processes
• Managing noise, including reality gap and sim 2 real
• Design and fabrication processes for evolved robots
• Real-world verification of evolved solutions

Contributions will be accepted as two-page extended abstracts, to be published in the GECCO companion proceedings, with subsequent presentation of the poster during the workshop itself.

Expected outcomes include an inventory of the main challenges, a technology survey of possible enablers and solutions (from our own and other fields), and a sketch roadmap towards a future of mainstream usage and adoption of evolutionary techniques in demanding robotic use cases.

Biographies

 

David Howard

Emma Hart

Prof. Hart received her PhD from the University of Edinburgh. She currently leads the Centre for Emergent Computing at Edinburgh Napier University where her research focuses on optimisation and continuous learning systems, with an emphasis applying methods from Artificial Immune Systems and HyperHeuristics. She has published extensively in the field of Artificial Immune Systems, with a particular interest in optimisation and self-organising systems such as swarm robotics. Her current interests relate to the development of optimisation algorithms that continuously learn through experience, and how collectives of algorithms can collaborate to form good problem solvers. She also has interests in more theoretical work relating to modelling the immune system to learn more about its computational properties. From January 2017, she will become Editor-in-Chief of Evolutionary Computing, She is also a member of the SIGEVO Executive Board and editor of the SIGEVO newsletter.

 

Gusz Eiben

Prof. Gusz Eiben is full professor of Artificial Intelligence on the Vrije Universiteit Amsterdam and Visiting Professor at the University of York, UK. He was one of the European early birds of Evolutionary Computing, focusing most recently on evolutionary robotics. He has co-authored the best selling textbook (with Jim Smith) and published over 200 research papers, including some in Nature and Science. He has been organizing committee member of practically all major evolutionary conferences (CEC, EP, EuroGP, EvoStar, FOGA, GECCO, PPSN) and editorial board member of various international journals (e.g., JEC, IEEE ToEC, GPEH, Frontiers Journal on Robotics and AI).

BENCHMARK — Good Benchmarking Practices for Evolutionary Computation

Summary

In the era of explainable and interpretable AI, it is increasingly necessary to develop a deep understanding of how algorithms work and how new algorithms compare to existing ones, both in terms of strengths and weaknesses. For this reason, benchmarking plays a vital role for understanding algorithms’ behavior. Even though benchmarking is a highly-researched topic within the evolutionary computation community, there are still a number of open questions and challenges that should be explored:
(i) most commonly-used benchmarks are too small and cover only a part of the problem space,
(ii) benchmarks lack the complexity of real-world problems, making it difficult to transfer the learned knowledge to work in practice,
(iii) we need to develop proper statistical analysis techniques that can be applied depending on the nature of the data,
(iv) we need to develop user-friendly, openly accessible benchmarking software. This enables a culture of sharing resources to ensure reproducibility, and which helps to avoid common pitfalls in benchmarking optimization techniques. As such, we need to establish new standards for benchmarking in evolutionary computation research so we can objectively compare novel algorithms and fully demonstrate where they excel and where they can be improved.

The topics of interest for this workshop include, but are not limited to:
· Performance measures for comparing algorithms behavior;
· Novel statistical approaches for analyzing empirical data;
· Selection of meaningful benchmark problems;
· Landscape analysis;
· Data mining approaches for understanding algorithm behavior;
· Transfer learning from benchmark experiences to real-world problems;
· Benchmarking tools for executing experiments and analysis of
experimental results.

We particularly welcome position papers addressing or identifying open challenges in benchmarking optimization techniques. Papers can be up to 8 pages long, in ACM format and excluding references. We emphasize that we also welcome short position statements, which can be as short as one abstract.

The schedule will be designed to encourage a high level of interactivity — expect a real workshop rather than (yet another) mini-conference!

Biographies

 

Tome Eftimov

Tome Eftimov is a postdoctoral research fellow at Stanford University. He received his Ph.D. degree from the Jožef Stefan Postgraduate School, Ljubljana, Slovenia, in January 2018. Since 2014 he has been a researcher at the Computer Systems Department, Jožef Stefan Institute, Ljubljana. He is involved in courses on probability and statistics, and statistical data analysis. The work related to Deep Statistical Comparison was presented as tutorial (i.e. IJCCI 2018, IEEE SSCI 2019) or invited lecture to several international conferences and universities. His research interests include statistics, heuristic optimization, natural language processing, machine learning, and representational learning.

William La Cava

Bill is a postdoctoral fellow at the University of Pennsylvania with the Institute for Biomedical Informatics. He received his Ph.D. from the University of Massachusetts Amherst under Professors Kourosh Danai and Lee Spector. His research focus is identifying causal models of disease from patient health records and genome wide association studies. His contributions in genetic programming include methods for local search, parent selection, and representation learning.

Boris Naujoks

Boris Naujoks is a professor for Applied Mathematics at TH Köln - Cologne University of Applied Sciences (CUAS). He joint CUAs directly after he received his PhD from Dortmund Technical University in 2011. During his time in Dortmund, Boris worked as a research assistant in different projects and gained industrial experience working for different SMEs. Meanwhile, he enjoys the combination of teaching mathematics as well as computer science and exploring EC and CI techniques at the Campus Gummersbach of CUAS. He focuses on multiobjective (evolutionary) optimization, in particular hypervolume based algorithms, and the (industrial) applicability of the explored methods.

 

Pietro Oliveto

Vanessa Volz

Vanessa Volz is an AI researcher at modl.ai (Copenhagen, Denmark), with focus in computational intelligence in games. She received her PhD in 2019 from TU Dortmund University, Germany, for her work on surrogate-assisted evolutionary algorithms applied to game optimisation. She holds B.Sc. degrees in Information Systems and in Computer Science from WWU Münster, Germany. She received an M.Sc. with distinction in Advanced Computing: Machine Learning, Data Mining and High Performance Computing from University of Bristol, UK, in 2014. Her current research focus is on employing surrogate-assisted evolutionary algorithms to obtain balance and robustness in systems with interacting human and artificial agents, especially in the context of games.

Thomas Weise

Thomas Weise obtained the MSc in Computer Science in 2005 from the Chemnitz University of Technology and his PhD from the University of Kassel in 2009. He then joined the University of Science and Technology of China (USTC) as PostDoc and subsequently became Associate Professor at the USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI) at USTC. In 2016, he joined Hefei University as Full Professor to found the Institute of Applied Optimization at the Faculty of Computer Science and Technology. Prof. Weise has more than seven years of experience as a full time researcher in China, having contributed significantly both to fundamental as well as applied research. He has more than 80 scientific publications in international peer reviewed journals and conferences. His book "Global Optimization Algorithms – Theory and Application" has been cited more than 730 times. He has acted as reviewer, editor, or programme committee member at 70 different venues.

DTEO — Workshop on Decomposition Techniques in Evolutionary Optimization

Summary

Tackling an optimization problem using decomposition consists in transforming (or re-modeling or re-thinking) it into multiple, a priori smaller and easier, problems that can be solved cooperatively. A number of techniques are being actively developed by the evolutionary computing community in order to explicitly or implicitly design decomposition with respect to four facets of an optimization problem: (i) the environmental parameters, (ii) the decision variables, (iii) the objective functions, and (iv) the available computing resources. The workshop aims to be a unified opportunity to report the recent advances in the design, analysis and understanding of evolutionary decomposition techniques and to discuss the current and future challenges in applying decomposition to the increasingly big and complex nature of optimization problems (e.g., large number of variables, large number of objectives, multi-modal problems, simulation optimization, uncertain scenario-based optimization) and its suitability to modern large scale compute environments (e.g., massively parallel and decentralized algorithms, large scale divide-and-conquer parallel algorithms, expensive optimization). The workshop focus is there-by on (but not limited to) the developmental, implementational, theoretical and applied aspects of:
• Large scale evolutionary decomposition, e.g., decomposition in decision space, gray-box techniques, co-evolutionary algorithms, grouping and cooperative techniques, decomposition for constraint handling
• Multi- and Many- objective decomposition, e.g., aggregation and scalarizing approaches, cooperative and hybrid island-based design, (sub-)population decomposition and mapping
• Parallel and distributed evolutionary decomposition, e.g., scalability with respect to decision and objective spaces, divide-and-conquer decentralized techniques, distribution of compute efforts, scalable deployments on heterogeneous and massively parallel computing environments
• Novel general-purpose decomposition techniques, e.g., machine-learning and model assisted decomposition, offline and on-line configuration of decomposition, search region decomposition and multiple surrogates, parallel expensive optimization
• Understanding and benchmarking decomposition techniques
• General purpose software tools and libraries for evolutionary decomposition

Biographies

Bilel Derbel

Bilel Derbel is an associate Professor, having a habilitation to supervise research (Maître de Conférences HDR), at the Department of Computer Science at the University of Lille, France, since 2007. He received his PhD in computer science from the University of Bordeaux (LaBRI, France) in 2006. In 2007, he spent one year as an assistant professor at the university of Aix-Marseille. He is a permanent member and the vice-head of the BONUS ‘Big Optimisation aNd Ultra-Scale Computing’ research group at Inria Lille-Nord Europe and CRIStAL, CNRS. He is a co-founder member of the International Associated Lab (LIA-MODO) between Shinshu Univ., Japan, and Univ. Lille, France, on ‘Massive optimisation and Computational Intelligence’. He has been a program committee member of evolutionary computing conferences such as GECCO, CEC, EvoOP, PPSN, and a regular journal reviewer in a number of reference journal in the optimisation field. He is an associate editor of the IEEE Transactions on Systems Man and Cybernetics: Systems. He co-authored more than fifty scientific papers. He was awarded best paper awards in SEAL'17, ICDCN'11, and was nominated for the best paper award in PPSN'18 and PPSN'14. His research topics are focused on the design and the analysis of combinatorial optimisation algorithms and high-performance computing. His current interests are on the design of adaptive distributed evolutionary algorithms for single- and multi-objective optimisation.

Ke Li

Ke Li is a Lecturer (Assistant Professor) in Data Analytics at the Department of Computer Science, University of Exeter. He earned his PhD from City University of Hong Kong. Afterwards, he spent a year as a postdoctoral research associate at Michigan State University. Then, he moved to the UK and took the post of research fellow at University of Birmingham. His current research interests include the evolutionary multi-objective optimization, automatic problem solving, machine learning and applications in water engineering and software engineering.

Xiaodong Li

Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in information science from University of Otago, Dunedin, New Zealand, respectively. He is a full professor at the School of Science (Computer Science and Software Engineering), RMIT University, Melbourne, Australia. His research interests include evolutionary computation, neural networks, machine learning, complex systems, multiobjective optimization, multimodal optimization (niching), and swarm intelligence. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member of IEEE CIS Task Force on Swarm Intelligence, a Vice-chair of IEEE CIS Task Force of Multi-Modal Optimization, and a former Chair of IEEE CIS Task Force on Large Scale Global Optimization. He was the General Chair of SEAL'08, a Program Co-Chair AI'09, a Program Co-Chair for IEEE CEC’2012, a General Chair for ACALCI’2017 and AI’17. He is the recipient of 2013 ACM SIGEVO Impact Award and 2017 IEEE CIS ""IEEE Transactions on Evolutionary Computation Outstanding Paper Award"".

Saúl Zapotecas

Saúl Zapotecas is a visiting Professor at Department of Applied Mathematics and Systems, Division of Natural Sciences and Engineering, Autonomous Metropolitan University, Cuajimalpa Campus (UAM-C). Saúl Zapotecas received the B.Sc. in Computer Sciences from the Meritorious Autonomous University of Puebla (BUAP). His M.Sc. and PhD in computer sciences from the Center for Research and Advanced Studies of the National Polytechnic Institute of Mexico (CINVESTAV-IPN). His current research interests include evolutionary computation, multi/many-objective optimization via decomposition, and multi- objective evolutionary algorithms assisted by surrogate models.

Qingfu Zhang

Qingfu Zhang is a Professor at the Department of Computer Science, City University of Hong Kong. His main research interests include evolutionary computation, optimization, neural networks, data analysis, and their applications. He is currently leading the Metaheuristic Optimization Research (MOP) Group in City University of Hong Kong. Professor Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions Cybernetics. He was awarded the 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is on the list of the Thomson Reuters 2016 and 2017 highly cited researchers in computer science. He is an IEEE fellow.

E-MaOP — Evolutionary Many-objective Optimization

Summary

he field of evolutionary multi-objective optimization has developed rapidly over the last 20 years, but the design of effective algorithms for addressing problems with more than three objectives (called many-objective optimization problems, MaOPs) remains a great challenge. First, the ineffectiveness of the Pareto dominance relation, which is the most important criterion in multi-objective optimization, results in the underperformance of traditional Pareto-based algorithms. Also, the aggravation of the conflict between convergence and diversity, along with increasing time or space requirement as well as parameter sensitivity, has become key barriers to the design of effective many-objective optimization algorithms. Furthermore, the infeasibility of solutions' direct observation can lead to serious difficulties in algorithms' performance investigation and comparison. All of these suggest the pressing need of new methodologies designed for dealing with MaOPs, new performance metrics and test functions tailored for experimental and comparative studies of evolutionary many-objective optimization (EMaO) algorithms.

List of Topics
We welcome high-quality original submissions addressing various topics related to evolutionary many-objective optimization, but are not limited to:
1) Machine learning methods for many-objective optimization,
2) Algorithms for evolutionary many-objective optimization, including search operators, mating selection, environmental selection and population initialization;
3) Performance indicators for evolutionary many-objective optimization;
4) Benchmark functions for evolutionary many-objective optimization;
5) Visualization techniques for evolutionary many-objective optimization;
6) Objective reduction techniques for evolutionary many-objective optimization;
7) Preference articulation and decision-making methods for evolutionary many-objective optimization;
8) Constraint handling methods for evolutionary many-objective optimization;
9) Evolutionary many-objective optimization in combinatorial/discrete problems;
10) Evolutionary many-objective optimization in dynamic environments;
11) Evolutionary many-objective optimization in large-scale problems.

Biographies

 

Rui Wang

 

Ran Cheng

Ran Cheng received the Ph.D. degree in computer science from University of Surrey, U.K. in 2016. He is currently a research fellow with CERCIA, School of Computer Science, University of Birmingham, U.K. His main research interests are evolutionary multi- and many-objective optimization, large-scale optimization and swarm intelligence. Dr. Cheng has published over 20 journal and conference papers. He is an editorial board member of Complex & intelligent Systems journal, the co-organizer of the 2017 IEEE CEC Competition on Evolutionary Many-objective Optimization, the co-chairs of the 2016 and 2017 IEEE Symposium on Model Based Evolutionary Algorithms (MBEA). He is the recipients of the Association of British Turkish Academics (ABTA) Doctoral Researcher Award (2015), the University of Surrey Vice-Chancellor’s Award (2015), and the Chinese Government Award for Outstanding Self-Financed Students Abroad (2016).

 

Guohua Wu

Guohua Wu received the B.S. degree in Information Systems and Ph.D degree in Operations Research from National University of Defense Technology, China, in 2008 and 2014, respectively. During 2012 and 2014, he was a visiting Ph.D student at University of Alberta, Edmonton, Canada. He is now a Professor at the School of Traffic and Transportation Engineering, Central South University, Changsha, China.
His current research interests include scheduling, evolutionary computation and machine learning. He has authored more than 60 referred papers including those published in IEEE TCYB, IEEE TSMCA, INS, COR. He serves as an Associate Editor of Swarm and Evolutionary Computation, an editorial board member of International Journal of Bio-Inspired Computation, a Guest Editor of Information Sciences and Memetic Computing. He is a regular reviewer of more than 20 journals including IEEE TEVC, IEEE TCYB, IEEE TFS.

 

Miqing Li

Miqing Li received the Ph.D. degree in computer science from Brunel University London, U.K. in 2015. He is currently a research fellow with CERCIA, School of Computer Science, University of Birmingham, U.K. His research interests are evolutionary multi- and many-objective optimization and its diverse applications. Dr. Li has published over 30 journal and conference papers.

Hisao Ishibuchi

EAPwU — Evolutionary Algorithms for Problems with Uncertainty

Summary

In many real-world optimisation problems, uncertainty is present in various forms. One prominent example is the sensitivity of the optimal solution to noise or perturbations in the environment. In such cases, handling uncertainty effectively can be critical for finding good robust solutions, in particular, when the uncertainty results in severe loss of quality. In recent years, uncertainty in its various forms has attracted a lot of attention from the evolutionary computation community. 

Optimisation problems can be categorised as one of four types, depending on the source of uncertainty: 1. robust problems, where the uncertainty arises in design or environmental variables, 2. noisy problems, where the uncertainty arises in objective space, 3. approximated problems, where approximated objective function(s) are that are subject to error, and 4. dynamic problems, where the objective function(s) changes over time.

Robust optimisation includes situations where the chosen design cannot be realised in a real-world setting without some error. Additionally, the solution may need to perform well under a set of different scenarios and/or under some assumptions of parameter drifts. Typically, explicit methods for handling this type of uncertainty rely on resampling the assumed scenario set in order to approximate the underlying robust fitness landscape. Noisy optimisation refers to problems in which the estimate of the quality of an individual is subject to some randomness, e.g. if the objective value is calculated from the output of a stochastic simulation or solver. In this case, the estimate of the expected objective value is usually based on several resamples of a given solution. However, methods that rely on resampling of solutions are often inadequate in situations where the evaluations are expensive.

These problems have been a concern for the community for a number of years, and there is a growing need for new methods to handle the various types of uncertainty in a wide variety of problem domains. In addition, the field stands to benefit greatly from new methods for assessing the performance of algorithms for optimisation in uncertain environments and development of suitable benchmark problems. This workshop is designed to bring together practitioners from different subfields in the evolutionary computing community to share their ideas and methods.


Particular topics of interest include, but are not limited to: 

Efficient methods for optimisation under uncertainty

Studies of the inherent capabilities of EAs to handle different types of uncertainty  

New ranking and selections operators for optimising under uncertainty

Meta-modelling for handling uncertainty

Methods for fitness approximation under uncertainty 

Quantifying the robustness of solutions

Real-World applications that suffer from various types of uncertainty

New benchmark problems for various types of uncertainty 

Design of experiments for estimating robust designs

Coping with multiple sources and forms of uncertainty

Multi-objective optimisation in uncertain contexts

Casting a problem with uncertainty as a multi-objective problem

Biographies

Jonathan Fieldsend

Jonathan Fieldsend is an Associate Professor in Computational Intelligence at the University of Exeter. He has a degree in Economics from Durham University, a Masters in Computational Intelligence from the University of Plymouth and a PhD in Computer Science from the University of Exeter. He has held postdoctoral positions as a Research Fellow (working on the interface of Bayesian modelling and optimisation) and as a Business Fellow (focusing on knowledge transfer to industry) prior to his appointment to an academic position at Exeter.

He has published widely on theoretical and applied aspects of evolutionary multi-objective optimisation, and also in the field of machine learning — and has ongoing interests on the interface between these areas. His theoretical work includes algorithm development and analysis, along with data structures required for efficient multi-objective optimisation and Pareto set maintenance. His applied work includes costly and uncertain industrial design problems, air traffic control safety systems, automating biological experiments and robust multi-objective routing.

He has previous been a workshop organiser at GECCO for VizGEC (Visualisation Methods in Genetic and Evolutionary Computation), SAEOpt (Surrogate-Assisted Evolutionary Optimisation) and EAPU (Evolutionary Algorithms for Problems with Uncertainty). He has been active within the evolutionary computation community as a reviewer and program committee member since 2003.

 

Ozgur Akman

Ozgur Akman is a Senior Lecturer in Mathematics at the University of Exeter. He has a BSc in Mathematics and a MSc in Bioengineering from Imperial College London, and a PhD in Mathematics from the University of Manchester. His research interests lie in the interface between applied mathematics, computer science and biology, focusing on the development of computational methods to systematically construct and analyse quantitative models of biochemical and neural networks. His earlier research used nonlinear dynamics techniques to identify the molecular mechanisms underlying the development of neurobiological motor disorders. A particular recent area of interest is the use of evolutionary computing methods to optimise large-scale systems biology models to experimental time series data. This important real-word optimisation problem is characterised by intrinsic uncertainty in the design space - due to the potential presence of multiple optima yielding similar fitness scores - and also in the objective space - due to experimental noise. 

Khulood Alyahya

Khulood Alyahya is a Research Fellow at the University of Exeter. She was awarded a PhD degree in Computer Science in 2016 from the University of Birmingham. She also has an MSc degree in Intelligent Systems Engineering from the same University where she was awarded the best student prize. Her PhD studies were on the landscape analysis of NP-hard problems. Her current research focuses on optimisation under multiple sources of uncertainty in both theoretical and applied settings, with application in the field of Computational Systems Biology. Her research includes extending landscape analysis tools to study the landscapes of robust optimisation problems.

Jürgen Branke

Jürgen Branke is Professor of Operational Research and Systems at Warwick Business School, University of Warwick, UK. He has been an active researcher in the field of Evolutionary Computation for over 20 years, has published over 170 papers in peer-reviewed journals and conferences, resulting in an H-Index of 52 (Google Scholar). His main research interests include optimization under uncertainty, simulation-based optimization and multi-objective optimization. Jürgen is Area Editor for the Journal of Heuristics and the Journal on Multi-Criteria Decision Analysis, and Associate Editor for the Evolutionary Computation Journal and IEEE Transactions on Evolutionary Computation.

EC+MCDM — Workshop on Evolutionary Computation + Multiple Criteria Decision Making (EC + MCDM)

http://blogs.exeter.ac.uk/ecmcdm/

Summary

In many real-world problems, several conflicting objectives need to be optimized simultaneously. Therefore, it is crucial to properly structure and solve the problem with relevant tools for supporting a decision maker. Multiple criteria decision making (MCDM) tools have been found to be useful in several such applications e.g. health care, education, environment, transportation, business and production. In recent years, there has also been growing interest in merging EC and MCDM techniques for several applications. This workshop will showcase research that is both at the interface of EC and MCDM as well as in the more traditional MCDM domain.
The workshop on Evolutionary Computation + Multiple Criteria Decision Making (EC + MCDM) to be held in GECCO 2020 aims to promote the research on theory and applications in the field. Topics of interest (but not limited to) include:
• Preference elicitation and representation
• Interactive multiobjective optimization or decision maker in the loop
• Visualization in EC + MCDM
• Aggregation/trade-off operators & algorithms
• Fuzzy logic based decision making techniques
• Bayesian and other decision making techniques
• Interactive multiobjective optimization for (computationally) expensive problems
• Using surrogates (or metamodels) in MCDM
• Hybridization of EC and MCDM
• Scalability in EC + MCDM
• MCDM and machine learning
• MCDM for Big data
• MCDM in real-world applications
• Exploring and using cognitive capabilities in MCDM
• Use of psychological tools to aid decision maker

Biographies

Tinkle Chugh

Dr. Tinkle Chugh is a Postdoctoral Research Fellow at the Department of Computer Science, University of Exeter, UK. He is also a Researcher at Palacký University, Olomouc, Czech Republic. He obtained his PhD degree in Mathematical Information Technology in 2017 from the University of Jyvaskyla, Finland. His thesis was a part of the “Decision Support for Complex Multiobjective Optimization Problems (DeCoMo)” project, where he collaborated with “Finland Distinguished Professor (FiDiPro)” Yaochu Jin from University of Surrey, UK. He received the best student paper award at IEEE Congress on Evolutionary Computation (IEEE CEC) 2017. His research interests are machine learning, data-driven optimization, evolutionary computation and decision making.

Richard Allmendinger

Richard is Business Engagement Lead of Alliance Manchester Business School and Lecturer in Data Science at The University of Manchester. Prior to Manchester, he worked at the Biochemical Engineering Department, University College London. He studied Business Engineering at the Karlsruhe Institute of Technology and the Royal Melbourne Institute of Technology and completed a PhD in Computer Science at The University of Manchester.

Richard's research interests are in the field of data science and in particular in the development and application of optimization, learning and analytics techniques to real-world problems arising in areas such as healthcare, manufacturing, sports, music, economics, and forensics. Much of his research has been funded by grants from Innovate UK, the Engineering and Physical Sciences Research Council (EPSRC), and industrial partners.

Richard is the Co-Founder of the IEEE CIS Task Force on Optimization Methods in Bioinformatics and Bioengineering, a Member of the IEEE CIS Bioinformatics and Bioengineering Technical Committee, the General Chair of the 2017 IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, and a Member of the Editorial Board of the Applied Soft Computing journal.

Jussi Hakanen

Dr Jussi Hakanen is a Senior Researcher at the Faculty of Information Technology at the University of Jyväskylä, Finland. He received MSc degree in mathematics and PhD degree in mathematical information technology, both from the University of Jyväskylä. His research is focused on multiobjective optimization with an emphasis on interactive multiobjective optimization methods and computationally expensive problems. He has participated in several industrial projects involving different applications of multiobjective optimization, e.g. in chemical engineering. He has been a visiting researcher in Cornell University, Carnegie Mellon, University of Surrey, University of Wuppertal, University of Malaga and the VTT Technical Research Center of Finland. He has a title of Docent (similar to Adjunct Professor in the US) in Industrial Optimization at the University of Jyväskylä, Finland.

ECADA — 10th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA)

Summary

Scope



The main objective of this workshop is to discuss hyper-heuristics and related methods, including but not limited to evolutionary computation methods, for generating and improving algorithms with the goal of producing solutions (algorithms) that are applicable to multiple instances of a problem domain. The areas of application of these methods include optimization, data mining and machine learning [1-18, 23].

Automatically generating and improving algorithms by means of other algorithms has been the goal of several research fields, including Artificial Intelligence in the early 1950s, Genetic Programming in the early 1990s, and more recently automated algorithm configuration [1] and hyper-heuristics [2]. The term hyper-heuristics generally describes meta-heuristics applied to a space of algorithms. While Genetic Programming has most famously been used to this end, other evolutionary algorithms and meta-heuristics have successfully been used to automatically design novel (components of) algorithms. Automated algorithm configuration grew from the necessity of tuning the parameter settings of meta-heuristics and it has produced several powerful (hyper-heuristic) methods capable of designing new algorithms by either selecting components from a flexible algorithmic framework [3,4] or recombining them following a grammar description [5,9].

Although most Evolutionary Computation techniques are designed to generate specific solutions to a given instance of a problem, one of the defining goals of hyper-heuristics is to produce solutions that solve more generic problems. For instance, while there are many examples of Evolutionary Algorithms for evolving classification models in data mining and machine learning, the work described in [8] employed a hyper-heuristic using Genetic Programming to create a generic classification algorithm which in turn generates a specific classification model for any given classification dataset, in any given application domain. In other words, the hyper-heuristic is operating at a higher level of abstraction compared to how most search methodologies are currently employed; i.e., it is searching the space of algorithms as opposed to directly searching in the problem solution space [9], raising the level of generality of the solutions produced by the hyper-heuristic evolutionary algorithm. In contrast to standard Genetic Programming, which attempts to build programs from scratch from a typically small set of atomic functions, hyper-heuristic methods specify an appropriate set of primitives (e.g., algorithmic components) and allow evolution to combine them in novel ways as appropriate for the targeted problem class. While this allows searches in constrained search spaces based on problem knowledge, it does not in any way limit the generality of this approach as the primitive set can be selected to be Turing-complete. Typically, however, the initial algorithmic primitive set is composed of primitive components of existing high-performing algorithms for the problems being targeted; this more targeted approach very significantly reduces the initial search space, resulting in a practical approach rather than a mere theoretical curiosity. Iterative refining of the primitives allows for gradual and directed enlarging of the search space until convergence.

As meta-heuristics are themselves a type of algorithm, they too can be automatically designed employing hyper-heuristics. For instance, in 2007, Genetic Programming was used to evolve mate selection in evolutionary algorithms [11]; in 2011, Linear Genetic Programming was used to evolve crossover operators [12]; more recently, Genetic Programming was used to evolve complete black-box search algorithms [13,14,16], SAT solvers [22], and FuzzyART category functions [23]. Moreover, hyper-heuristics may be applied before deploying an algorithm (offline) [5] or while problems are being solved (online) [9], or even continuously learn by solving new problems (life-long) [19]. Offline and life-long hyper-heuristics are particularly useful for real-world problem solving where one can afford a large amount of a priori computational time to subsequently solve many problem instances drawn from a specified problem domain, thus amortizing the a priori computational time over repeated problem solving. Recently, the design of Multi-Objective Evolutionary Algorithm components was automated [21].

Very little is known yet about the foundations of hyper-heuristics, such as the impact of the meta-heuristic exploring algorithm space on the performance of the thus automatically designed algorithm. An initial study compared the performance of algorithms generated by hyper-heuristics powered by five major types of Genetic Programming [18]. Another avenue for research is investigating the potential performance improvements obtained through the use of asynchronous parallel evolution to exploit the typical large variation in fitness evaluation times when executing hyper-heuristics [20].


Content
---

We welcome original submissions on all aspects of Evolutionary Computation for the Automated Design of Algorithms, in particular, evolutionary computation methods and other hyper-heuristics for the automated design, generation or improvement of algorithms that can be applied to any instance of a target problem domain. Relevant methods include methods that evolve whole algorithms given some initial components as well as methods that take an existing algorithm and improve it or adapt it to a specific domain. Another important aspect in automated algorithm design is the definition of the primitives that constitute the search space of hyper-heuristics. These primitives should capture the knowledge of human experts about useful algorithmic components (such as selection, mutation and recombination operators, local searches, etc) and, at the same time, allow the generation of new algorithm variants. Examples of the application of hyper-heuristics, including genetic programming and automatic configuration methods, to such frameworks of algorithmic components are of interest to this workshop, as well as the (possibly automatic) design of the algorithmic components themselves and the overall architecture of metaheuristics. Therefore, relevant topics include (but are not limited to):
- Applications of hyper-heuristics, including general-purpose automatic algorithm configuration methods for the design of metaheuristics, in particular evolutionary algorithms, and other algorithms for application domains such as optimization, data mining, machine learning, image processing, engineering, cyber security, critical infrastructure protection, and bioinformatics.
- Novel hyper-heuristics, including but not limited to genetic programming based approaches, automatic configuration methods, and online, offline and life-long hyper-heuristics, with the stated goal of designing or improving the design of algorithms.
- Empirical comparison of hyper-heuristics.
- Theoretical analyses of hyper-heuristics.
- Studies on primitives (algorithmic components) that may be used by hyper-heuristics as the search space when automatically designing algorithms.
- Automatic selection/creation of algorithm primitives as a preprocessing step for the use of hyper-heuristics.
- Analysis of the trade-off between generality and effectiveness of different hyper-heuristics or of algorithms produced by a hyper-heuristic.
- Analysis of the most effective representations for hyper-heuristics (e.g., Koza style Genetic Programming versus Cartesian Genetic Programming).
- Asynchronous parallel evolution of hyper-heuristics.


Interactive Activity
--------

One of the ECADA traditions is to close with an interactive discussion panel focusing on challenges in the field. This is particular pertinent because this workshop is a nexus point for collaboration and discussion between several sometimes disjointed communities, namely genetic programming, hyper-heuristics, and automatic algorithm configuration.

Biographies

John R. Woodward

John R. Woodward is head of the Operational Research Group (http://or.qmul.ac.uk/) at QMUL. He holds a BSc in Theoretical Physics, an MSc in Cognitive Science and a PhD in Computer Science, all from the University of Birmingham. His research interests include Automated Software Engineering, Artificial Intelligence/Machine Learning and in particular Genetic Programming. Publications are at (https://scholar.google.co.uk/citations?user=iZIjJ80AAAAJ&hl=en), and current EPSRC grants are at (https://gow.epsrc.ukri.org/NGBOViewPerson.aspx?PersonId=-485755). Public engagement articles are at (https://theconversation.com/profiles/john-r-woodward-173210/articles). He has worked in industrial, military, educational and academic settings, and been employed by EDS, CERN and RAF and three UK Universities (Birmingham, Nottingham, Stirling).

Daniel R. Tauritz

Daniel R. Tauritz is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University, a cyber consultant for Sandia National Laboratories, a Guest Scientist at Los Alamos National Laboratory (LANL), the founding director of AU's Biomemetic National Security Artificial Intelligence (BONSAI) Laboratory, founding academic director of the LANL/AU Cyber Security Sciences Institute, and the Chief Cyber AI Strategist of the Auburn Cyber Research Center. He received his Ph.D. in 2002 from Leiden University for Adaptive Information Filtering employing a novel type of evolutionary algorithm. He served previously as GECCO 2010 Late Breaking Papers Chair, GECCO 2012 & 2013 GA Track Co-Chair, GECCO 2015 ECADA Workshop Co-Chair, GECCO 2015 MetaDeeP Workshop Co-Chair, GECCO 2015 Hyper-heuristics Tutorial co-instructor, and GECCO 2015 CBBOC Competition co-organizer. For several years he has served on the GECCO GA track program committee, the Congress on Evolutionary Computation program committee, and a variety of other international conference program committees. His research interests include the design of hyper-heuristics and self-configuring evolutionary algorithms and the application of computational intelligence techniques in cyber security, critical infrastructure protection, and program understanding. He was granted a US patent for an artificially intelligent rule-based system to assist teams in becoming more effective by improving the communication process between team members.

Emma Hart

Prof. Hart received her PhD from the University of Edinburgh. She currently leads the Centre for Emergent Computing at Edinburgh Napier University where her research focuses on optimisation and continuous learning systems, with an emphasis applying methods from Artificial Immune Systems and HyperHeuristics. She has published extensively in the field of Artificial Immune Systems, with a particular interest in optimisation and self-organising systems such as swarm robotics. Her current interests relate to the development of optimisation algorithms that continuously learn through experience, and how collectives of algorithms can collaborate to form good problem solvers. She also has interests in more theoretical work relating to modelling the immune system to learn more about its computational properties. From January 2017, she will become Editor-in-Chief of Evolutionary Computing, She is also a member of the SIGEVO Executive Board and editor of the SIGEVO newsletter.

ECPERM — Evolutionary Computation for Permutation Problems

Summary

Permutation-based optimization problems are a class of combinatorial optimization problems that naturally arises in many real world applications and theoretical scenarios where an optimal ordering or ranking of items has to be found with respect to one or more objective criteria. Some popular examples are: flowshop scheduling problem, traveling salesman problem, quadratic assignment problem and linear ordering problem.
Since the first paper on the traveling salesman problem in 1985 by Goldberg, permutation problems have been recurrently addressed in the field of Evolutionary Computation (EC) from a wide variety of perspectives. Evolutionary algorithms, fitness landscape analysis, genotypic representations or probabilistic models on rankings are only a few of the topics that have been discussed in the literature.
In modern combinatorics, permutations are probably among the richest combinatorial structures. Motivated principally by their versatility - ordered set of items, collection of disjoint cycles, transpositions, matrices or graphs - permutations appear in a vast range of domains, thus making permutation problems a very special case where ideas and concepts originated from classical mathematical fields, such as algebra, geometry, and probability theory, can be exploited and used in the design of new metaheuristics and genetic operators.
All these aspects have recently motivated a strong and ongoing research interest towards permutation problems in EC. Therefore, this workshop aims to highlight the most recent advances in the field and to bring together the EC researchers working in all the aspects of permutation problems.
Authors are invited to submit their original and unpublished work in the areas including, but not limited to:
- EC applications to the flowshop scheduling problem
- EC applications to the traveling salesman problem
- EC applications to the linear ordering problem
- EC applications to the quadratic assignment problem
- EC applications to any kind of single or multiple objective(s) permutation-based optimization problem
- Novel permutation-based optimization problems in EC
- Fitness landscape analysis of permutation-based optimization problems
- Theoretical analysis of permutation search spaces, meta-heuristics and hardness of problem instances
- Algebraic models for EC in permutation-based search spaces
- Probabilistic models for EC in permutation-based search spaces
- Permutation genotypic representations for EC techniques
- Experimental evaluations and comparisons of EC techniques for permutation-based optimization problems

Biographies

 

Marco Baioletti

Josu Ceberio Uribe

Josu Ceberio received the bachelor degree in Computer Sciece from the University of the Basque Country in 2007, and two years later he took his masters degree in Computer Science from the same university. Since 2010, he has been member of the Intelligent Systems Group where he obtained, in 2014, the PhD in Computer Science. Since 2014, he is lecturer at the University of the Basque Country, and currently, he is affiliated to the department of Computer Science and Artificial Intelligence at the Faculty of Computer Science. He has co-authored more than 30 scientific publications in different journals and international conferences covering topics such as permutation-based combinatorial optimization problems, estimation of distribution algorithms, multi-objectivisation, elementary landscape decomposition and parameterized instance complexity.

John McCall

John McCall is a Professor of Computing in the IDEAS Research Institute at Robert Gordon University in Scotland. Originally a pure mathematician (algebraic topology), he has over twenty years research experience in naturally-inspired computing. Major themes of his research include the development and analysis of novel metaheuristics, particularly markov-network EDAs, and probabilistic modelling for optimisation and learning. Application areas of his research include medical decision support, drilling rig market analysis, analysis of biological sequences, staff rostering and scheduling, image analysis and bio-control. Algorithms developed from his research have been implemented as commercial software. Prof. McCall has over 90 publications in books, international journals and conferences and he chairs the IEEE ECTC Task Force in Evolutionary Algorithms based on Probabilistic Models.

 

Alfredo Milani

Alfredo Milani received the Doctor degree in computer science from University of Pisa, Pisa, Italy, in 1987. He is an Associate Professor with the Department of Computer Science, University of Perugia, Italy, where he is the Head of the Knowledge and IT Laboratory. He has been a Visiting Associate Professor with Hong Kong Baptist University, Hong Kong, and a Visiting Scientist with Jet Propulsion Laboratory, Pasadena, CA, USA. His current research interests include artificial intelligence (AI), planning and agent systems, evolutionary meta-heuristics, data mining, and semantic proximity measures. Prof. Milani was the Chair or the Co-Chair of several international conferences in the AI area.

EvoSoft — Evolutionary Computation Software Systems

Summary

Evolutionary computation (EC) methods are applied in many different domains. Therefore, soundly engineered, reusable, flexible, user-friendly, and interoperable software systems are more than ever required to bridge the gap between theoretical research and practical application. However, due to the heterogeneity of application domains and the large number of EC methods, the development of such systems is both, time consuming and complex. Consequently many EC researchers still implement individual and highly specialized software which is often developed from scratch, concentrates on a specific research question, and does not follow state of the art software engineering practices. By this means the chance to reuse existing systems and to provide systems for others to build their work on is not sufficiently seized within the EC community. In many cases the developed systems are not even publicly released, which makes the comparability and traceability of research results very hard. This workshop concentrates on the importance of high-quality software systems and professional software engineering in the field of EC and provides a platform for EC researchers to discuss the following and other related topics:

  • development and application of generic and reusable EC software systems
  • architectural and design patterns for EC software systems
  • software modeling of EC algorithms and problems
  • open-source EC software systems
  • expandability, interoperability, and standardization
  • comparability and traceability of research results
  • graphical user interfaces and visualization
  • comprehensive statistical and graphical results analysis
  • parallelism and performance
  • usability and automation
  • comparison and evaluation of EC software systems

Biographies

Stefan Wagner

Stefan Wagner received his MSc in computer science in 2004 and his PhD in technical sciences in 2009, both from Johannes Kepler University Linz, Austria. From 2005 to 2009 he worked as associate professor for software project engineering and since 2009 as full professor for complex software systems at the Campus Hagenberg of the University of Applied Sciences Upper Austria. From 2011 to 2018 he was also CEO of the FH OÖ IT GmbH, which is the IT service provider of the University of Applied Sciences Upper Austria. Dr. Wagner is one of the founders of the research group Heuristic and Evolutionary Algorithms Laboratory (HEAL) and is project manager and head architect of the open-source optimization environment HeuristicLab. He works as project manager and key researcher in several R&D projects on production and logistics optimization and his research interests are in the area of combinatorial optimization, evolutionary algorithms, computational intelligence, and parallel and distributed computing.

Michael Affenzeller

Michael Affenzeller has published several papers, journal articles and books dealing with theoretical and practical aspects of evolutionary computation, genetic algorithms, and meta-heuristics in general. In 2001 he received his PhD in engineering sciences and in 2004 he received his habilitation in applied systems engineering, both from the Johannes Kepler University Linz, Austria. Michael Affenzeller is professor at the University of Applied Sciences Upper Austria, Campus Hagenberg, and head of the research group Heuristic and Evolutionary Algorithms Laboratory (HEAL).

GI@GECCO — The Ninth Genetic Improvement Workshop (2020)

Summary

The Ninth Genetic Improvement Workshop (2020)
==========
Genetic Improvement is the application of search to the functional and non-functional improvement of software.

In 2015 the inaugural Genetic Improvement Workshop was held in conjunction with GECCO. The workshop was a big success, with over 40 attendees, receiving 16 submissions and incorporating a lively and packed schedule. Feedback from the post-workshop surveys was overwhelmingly positive.

This event was repeated in 2016, and 2017 with strong participation from the community and a full schedule in each. Two GI workshops were held each year in 2018 and 2019 as part of the International Conference on Software Engineering (ICSE), whilst the 5th continued to promote GI to the evolutionary computing at GECCO 2018 in Japan and the 7th at GECCO 2019 in Prague.

It is hoped to continue both lines so that the 8th international GI workshop will be held at ICSE 2020 in May and we hope to hold the 9th at GECCO in July. The GI@GECCO workshop continues to fulfill a key role in promoting new work, building community for new researchers, and setting new directions in Genetic Improvement. The quality of submissions and presentations continues to be strong. Feedback from all of these events has been highly positive with lively discussions and ongoing post-conference collaborations.

To build on the success of these past events we propose a ninth event to be held at GECCO in Cancun in 2020. The focus of this event will be on the presentation of new work of high quality work and the development of joint proposals for directions of research with strong potential. We expect this workshop to attract participants from the key groups of the field as well as new participants. Our intention is to focus on quality so we propose an intensive single session workshop to showcase the four to six best new works in this field.

A key focus of the workshop will be on research that describes and/or exploits application domain knowledge or learned features of the search landscape to improve the search process. This acknowledges that as the number and diversity of publications in the field grows there is scope to analyse and condense observations from these to identify common patterns and strategies. These can help inform promising directions for future research to improve, both the speed and power of GI applications to make them even more applicable to industrial settings.

The organisers, Brad Alexander, Alexander (Sandy) Brownlee, Saemundur Haraldsson, Markus Wagner, John Woodward are key members of the GI community and were key participants in previous GI workshops and were organisers of the successful 7th GI workshop in Prague in 2019.



Interest to the Community
=======
The GI community is growing in size and impact with a large body of recent work aiming to gain a better understanding of how exploiting knowledge of the search domain and its features can improve the performance of GI frameworks. This improved performance has the potential to greatly increase the applicability of GI in industrial settings. The sharing of new findings from diverse application domains will speed up new developments in the field and set up new collaborations.

Previous workshops have been well attended and well received by participants with an average of approximately 30 participants over past events and active and broad discussions addressing open questions in the field.
Potential Attendees and Submission Estimates
========
We expect submissions from anyone who has implemented an evolutionary or search algorithm for optimising software. We are particularly keen to bring together experts in automated program improvement (including bug fixing, energy, memory, and efficiency optimisation, benchmarking, landscape analysis, workflows and deployment) and those with recent interest and those new to the concept. GI 2020 will be the sixth session at GECCO dedicated to this topic.

We anticipate around 10 submissions, and experience from recent years suggests sizeable interest from those who have yet to conduct research in GI; we anticipate an audience in the region of 30.

To give the session focus and structure we are requesting a single session. The session will consist of a series of short presentations of accepted papers followed by a panel discussion of key issues in the integration of GI into real settings (including the scope and applicability of current GI and how to integrate and improve its reach into real settings).

We plan to host a social event and expect to raise funds for student bursaries for PhD students presenting at the workshop for the first time. Given the student mentoring was highly valued, we intend to repeat this success.

We have extensive knowledge of the SBSE field and personal contact (through collaborations, projects, and invited talks) with the most regarded authors. As well as being an active part of major online discussion groups, we intend to make extensive use of these, Twitter and other electronic media to publicise GI 2020.




GI 2020 Website
===
In addition to Twitter etc. and the main GECCO 2020 web pages we
provide a workshop website at geneticimprovementofsoftware.com.

Biographies

Bradley Alexander

Brad Alexander is a member of the Optimisation and Logistics Group at the University of Adelaide. His research interests include program optimisation, rewriting, genetic-programming (GP) - especially the discovery of recurrences and search-based-software-engineering. He is currently supervising projects in evolutionary art and in applications of search based software engineering to energy conservation and monitoring in mobile platforms. He has also supervised successful projects in the evolution of control algorithms for robots, the evolution of three-dimensional geological models, and the synthesis and optimisation of artificial water distribution networks, and using background optimisation to improve the performance of instruction set simulators (ISS)'s.

 

Alexander (Sandy) Brownlee

Alexander (Sandy) Brownlee is a Lecturer in the Division of Computing Science and Mathematics at the University of Stirling. His main topics of interest are in search-based optimisation methods and machine learning, with applications in civil engineering, transportation and SBSE. Within SBSE, he is interested in automated bug-fixing and improvement of non-functional properties such as run-time and energy consumption; how these different objectives interact with each other; and novel approaches to mutating code. He is also one of the developers of Gin, an open-source toolkit for experimentation with Genetic Improvement on real-world software projects. http://www.cs.stir.ac.uk/~sbr/

Saemundur O. Haraldsson

Saemundur O. Haraldsson is a Lecturer at the University of Stirling. He has multiple publications on Genetic Improvement, including two that have received best paper awards; in 2017’s GI and ICTS4eHealth workshops. Additionally, he co-authored the first comprehensive survey on GI which was published in 2017. He has been invited to give multiple talks on the subject, including three Crest Open Workshops and for an industrial audience in Iceland. His PhD thesis (submitted in May 2017) details his work on the world's first live GI integration in an industrial application. Saemundur has previously given a tutorial on GI at PPSN 2018.

Markus Wagner

Markus Wagner is a Senior Lecturer at the School of Computer Science, University of Adelaide, Australia. He has done his PhD studies at the Max Planck Institute for Informatics in Saarbruecken, Germany and at the University of Adelaide, Australia. For the outcomes of his studies, he has received the university's Doctoral Research Medal - the first for this school.
His research topics range from mathematical runtime analysis of heuristic optimisation algorithms and theory-guided algorithm design to applications of heuristic methods to renewable energy production, professional team cycling and software engineering. So far, he has been a program committee member 30 times, and he has written over 100 articles with over 100 different co-authors. He is on SIGEVO's Executive Board and serves as the first ever Sustainability Officer. He has contributed to GECCOs as Workshop Chair and Competition Chair, and he has chaired several education-related committees within the IEEE CIS.

John R. Woodward

John R. Woodward is head of the Operational Research Group (http://or.qmul.ac.uk/) at QMUL. He holds a BSc in Theoretical Physics, an MSc in Cognitive Science and a PhD in Computer Science, all from the University of Birmingham. His research interests include Automated Software Engineering, Artificial Intelligence/Machine Learning and in particular Genetic Programming. Publications are at (https://scholar.google.co.uk/citations?user=iZIjJ80AAAAJ&hl=en), and current EPSRC grants are at (https://gow.epsrc.ukri.org/NGBOViewPerson.aspx?PersonId=-485755). Public engagement articles are at (https://theconversation.com/profiles/john-r-woodward-173210/articles). He has worked in industrial, military, educational and academic settings, and been employed by EDS, CERN and RAF and three UK Universities (Birmingham, Nottingham, Stirling).

GreenAI — Green AI: Evolutionary and machine learning solutions in environment, renewable and ecologically-aware scenarios

Summary

Artificial intelligence, machine learning, and computational intelligence methods are essential components of any renewable energy or ecologically conscious activity action. This is derived from the fact that to make renewable energies operational, efficient and viable it is necessary to model and/or optimize a myriad of complex phenomena and processes.

Recent advances in sensors, data management, and cloud computing are transforming the environment for operations managers in these industries. Large, rich datasets can be readily assembled from diverse sources with substantial computational power available for analytics.

This creates a fertile environment for the application of computational intelligence. Prediction, classification and optimization algorithms can support decision-makers in the management of highly expensive resources, where even small percentage cost reductions can amount to millions of dollars. The application of computational intelligence can be transformative, leading to large-scale efficiency and major changes in operations.
There is, however, an area that has been neglected by researchers and industry: the ecological impact of artificial intelligence itself.

Only recently some light has been cast in this direction. On one hand, it has been forecasted that by 2030 half of the world’s electric energy consumption with be attributed to computing facilities. On the other hand, recent studies show the design and training of a state of the art machine learning models produced the same amount of CO2 as six medium cars during their lifespan. This raises many concerns on how to make an ecologically-viable artificial intelligence.

In this direction, it has been hypothesized that cloud and mobile computing, transfer learning, domain adaptation, model reuse, active learning, and evolutionary computing, among others, could contribute to produce an eco-savvy AI. However, this is an area that needs yet to be properly explored both from theoretical and practical points of view.

There is a growing global interest in moving towards a digital transformation of the economy. This transformation is certainly needed but it should also take into account the ecological impact and viability. It is then the moment to focus on topics like these.

The objective of this workshop is to serve as a convergence hub for researchers and academics working on the application of evolutionary computing and machine learning to both the area of energy generation, production, and transformation with an ecologically-minded approach and the construction of eco-aware machine learning models and algorithms.

Applications papers, like those related -but not limited- to:
- evolutionary and optimization methods for ecology-aware and energy-aware artificial intelligence methods,
- understanding and minimization of the ecological impact of computing,
- renewables, wind, wave, and tidal energy production,
- oil, gas or coal transition and hybrid energy models,
- predictive maintenance, and
- logistics and supply chain optimization.

Relevant methods and techniques like:
- multi-component optimization,
- simulation-optimization,
- energy-aware AutoML approaches,
- transfer learning and domain adaptation,
- probabilistic modeling with large real-world datasets, and
- prediction, classification, and clustering with large real-world datasets.

Biographies

Nayat Sánchez-Pi

Nayat Sánchez-Pi is a Professor of Artificial Intelligence and Human-Computer Interaction at the Rio de Janeiro State University where she co-leads the Research on Intelligence and Optimisation Group (RIO). Prof. Sánchez-Pi research interests range from artificial intelligence, machine learning and data mining to ambient intelligence, ubiquitous computing, and multi-agent systems. She has led numerous energy and petroleum industry research projects applying evolutionary computation, machine learning, and other artificial intelligence methods.

Luis Martí

Luis is currently the scientific director of Inria Chile, the Chilean Center of Inria, the French National Institute for Computational Sciences. Before that, he was a senior researcher of the TAU team at Inria Saclay since 2015. He was also an Adjunct Professor (tenured) at the Institute of Computing of the Universidade Federal Fluminense. Previous to that, Luis was a CNPq Young Talent of Science Fellow at the Applied Robotics and Intelligence Lab of the Department of Electrical Engineering of the Pontifícia Universidade Católica do Rio de Janeiro, Brazil.
Luis did his Ph.D. at the Group of Applied Artificial Intelligence of the Department of Informatics of the Universidad Carlos III de Madrid, Madrid, Spain and got his Computer Science degree from the University of Havana. He is mainly interested in artificial intelligence, and, in particular, machine learning, neural networks, evolutionary computation, optimization, machine learning, fuzzy logic, hybrid systems and all that.

IAM 2020 — 5th Workshop on Industrial Applications of Metaheuristics

Summary

This workshop proposes to present and debate about the current achievements of applying metaheuristics to solve real-world problems in industry and the future challenges, focusing on the (always) critical step from the laboratory to the shop floor. A special focus will be given to the discussion of which elements can be transferred from academic research to industrial applications and how industrial applications may open new ideas and directions for academic research. Authors can submit papers, presenting their last achievements, and their presentations will be combined with an Invited Talk of a speaker with a recognized background working with industry in the field of metaheuristics and EC. The event is co-organized by ArcelorMittal (biggest steel making industry in the World) and IRIDIA (The AI Lab of the ULB, Université Libre de Bruxelles).

Biographies

Silvino Fernandez Alzueta

Silvino Fernández is an R&D Engineer at the Global R&D Department of ArcelorMittal for more than 15 years. He develops his activity in the ArcelorMittal R&D Centre of Asturias, in the framework of the Business and TechnoEconomic project Department. He has a Master Science degree in Computer Science, obtained at University of Oviedo in Spain, and also a Ph.D. in Engineering Project Management obtained in 2015. His main research interests are analytics, metaheuristics and swarm intelligence, and he has broad experience using these kind of techniques in industrial environment to optimize production processes. His paper ‘Scheduling a Galvanizing Line by Ant Colony Optimization‘ obtained the best paper award in the ANTS conference in 2014.

Pablo Valledor Pellicer

Pablo Valledor is an R&D engineer of the Global R&D Asturias Centre at ArcelorMittal (world's leading integrated steel and mining company), working at the Business & Technoeconomic area. He obtained his MS degree in Computer Science in 2006 and his PhD on Business Management in 2015, both from the University of Oviedo. He worked for the R&D department of CTIC Foundation (Centre for the Development of Information and Communication Technologies in Asturias) until February 2007, when he joined ArcelorMittal. His main research interests are metaheuristics, multi-objective optimization, analytics and operations research.

Thomas Stützle

Thomas Stützle is a senior research associate of the Belgian F.R.S.-FNRS working at the IRIDIA laboratory of Université libre de Bruxelles (ULB), Belgium. He received the Diplom (German equivalent of M.S. degree) in business engineering from the Universität Karlsruhe (TH), Karlsruhe, Germany in 1994, and his PhD and his habilitation in computer science both from the Computer Science Department of Technische Universität Darmstadt, Germany, in 1998 and 2004, respectively. He is the co-author of two books about ``Stochastic Local Search: Foundations and Applications and ``Ant Colony Optimization and he has extensively published in the wider area of metaheuristics including 20 edited proceedings or books, 8 journal special issues, and more than 190 journal, conference articles and book chapters, many of which are highly cited. He is associate editor of Computational Intelligence, Swarm Intelligence, and Applied Mathematics and Computation and on the editorial board of seven other journals including Evolutionary Computation and Journal of Artificial Intelligence Research. His main research interests are in metaheuristics, swarm intelligence, methodologies for engineering stochastic local search algorithms, multi-objective optimization, and automatic algorithm configuration. In fact, since more than a decade he is interested in automatic algorithm configuration and design methodologies and he has contributed to some effective algorithm configuration techniques such as F-race, Iterated F-race and ParamILS. His 2002 GECCO paper on "A Racing Algorithm For Configuring Metaheuristics" (joint work with M. Birattari, L. Paquete, and K. Varrentrapp) has received the 2012 SIGEVO impact award.

iGECCO — Interactive Methods @ GECCO

Summary

As nature-inspired methods have evolved, it has become clear that optimising towards a quantified fitness function is not always feasible, particularly where part or all of the evaluation of a candidate solution is inherently subjective. This is particularly the case when applying search algorithms to problems such as the generation of art and music. In other cases, optimising to a fitness function might result in a highly optimal solution that is not well suited to implementation in the real world. Incorporating a human into the optimisation process can yield useful results in both examples, and as such the work on interactive evolutionary algorithms (IEAs) has matured in recent years. This proposed workshop will provide an outlet for this research for the GECCO audience. Particular topics of interest are:

* Interactive generation of solutions.
* Interactive evaluation of solutions.
* Psychological aspects of IEAs.
* Multi- and many-objective optimisation with IEAs.
* Machine learning approaches within IEAs.
* Novel applications of IEAs.

Most IEAs focus on either asking the user to generate solutions to a problem with which they are interacting, or asking them to evaluate solutions that have been generated by an evolutionary process. To enable users to generate solutions it is necessary to develop mechanisms by which they can interact with a given solution representation. Solution evaluation requires the display of the solution (e.g., with a visualisation of the chromosome) so that the user can choose between two or more solutions having identified characteristics that best suit them.

As well as the basic interaction and solution evaluation, IEAs bring with them additional considerations through the inclusion of the user. A prime example of such a consideration is "user fatigue". The many iterations required by most nature-inspired methods can equate to a very large number of interactions between the user and system. Over many repeated interactions the user can become fatigued, so methods aimed at addressing this (and other similar effects) are of great importance to the future development of IEAs.

Biographies

 

Matthew Johns

Dr Matt Johns is a Research Fellow in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter. He obtained a PhD in Computer Science from the University of Exeter developing methods for incorporating domain expertise into evolutionary algorithms. Following the submission of his PhD thesis, he worked as an Associate Research Fellow in the Centre for Water Systems developing decision support tools to aid in the optimal design of waste water treatment systems. He then went on to work on the Human-Computer Optimisation for Water Systems Planning and Management project, developing new approaches to the design and management of water systems by incorporating visual analytics, heuristic optimisation and machine learning. His research interests include evolutionary optimisation, water systems optimisation, human-computer interaction and interactive visualisation.

 

Nick Ross

Nick Ross is a Computer Science PhD student in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter. His interests lie in nature-inspired computation, gamification, and artificial intelligence.

Ed Keedwell

Prof. Keedwell is an Associate Professor and Director of Research for Computer Science. He has personal research interests in nature-inspired computing techniques (e.g. genetic algorithms, neural networks, cellular automata) and hyper-heuristics, exploring their application to a variety of difficult problems in bioinformatics and engineering. He leads the Nature Inspired computing research group focusing on applied optimisation and has been involved with successful funding applications totalling over £2 million from the EPSRC, Innovate UK, EU and industry.

 

Herman Mahmoud

Dr Herman Mahmoud is a Research Fellow in the Centre for Water Systems, College of Engineering and Physical Sciences, University of Exeter. Herman graduated at the University of Dohuk in 2010 with MSc degree in Water Resources Engineering, and obtained his PhD in Engineering at the University of Exeter. His PhD thesis presented a novel real-time methodology for efficient and effective operational, short time response to an unplanned failure event in a WDS. His general research interests include real-time management in smart water systems, asset management in water systems, water resources management, decision support systems in water systems , water systems optimization, and hydraulic structures.

 

David Walker

Lecturer in Computer Science
University of Plymouth

IWLCS 2020 — 23rd International Workshop on Learning Classifier Systems

Summary

In the research field of Evolutionary Machine Learning (EML), Learning Classifier Systems (LCS) provide a powerful technique which received a huge amount of research attention over nearly four decades. Since John Holland’s formalization of the Genetic Algorithm (GA) and his conceptualization of the first LCS – the Cognitive System 1 (CS-I) – in the 1970’s, the LCS paradigm has broadened greatly into a framework encompassing many algorithmic architectures, knowledge representations, rule discovery mechanisms, credit assignment schemes, and additional integrated heuristics. This specific kind of EML technique bears a great potential of applicability to various problem domains such as behavior modeling, online-control, function approximation, classification, prediction, and data mining. Clearly, these systems uniquely benefit from their adaptability, flexibility, minimal assumptions, and interpretability.
The working principle of a LCS is to evolve a set of IF(condition)-THEN(action) rules, so-called classifiers, which partition the problem space into smaller subspaces. Thereby, each of these elements encoding the system’s knowledge can either be represented by rather straight-forward schemes such as IF-THEN rules, or be realized by more complex models such as Artificial Neural Networks. Accordingly, LCSs are also enabled to carry out different kinds of local predictions for the various niches of the problem space. The size and shape of the subspaces each single classifier covers, is optimized via a steady-state Genetic Algorithm (GA) which pursues a globally maximally general subspace, but at the same time strives for maximally accurate local prediction. This tension was formalized as the “Generalization Hypothesis” by Stewart Wilson in 1995 when he presented today’s mostly investigated LCS derivative – the Extended Classifier System (XCS). According to the working principle of LCS/XCS, one could also understand a generic LCS as an Evolving Ensemble of local models which in combination obtain a problem-dependent prediction output. This raises the question: How can we model these classifiers? Or put another way: Which kind of machine learning and evolutionary computation algorithms can be utilized within the well-understood algorithmic structure of an LCS? For example, Radial Basis Function Interpolation and Approximation Networks, Multi-Layer Perceptrons (MLP), as well as Support Vector Machines (SVM) have been used to model classifier predictions.
This workshop opens a forum for ongoing research in the field of LCS as well as for the design and implementation of novel LCS-style EML systems, that make use of evolutionary computation techniques to improve the prediction accuracy of the evolved classifiers. Furthermore, it shall solicit researchers of related fields such as (Evolutionary) Machine Learning, (Multi-Objective) Evolutionary Optimization, Neuroevolution, etc. to bring in their experience. In the era of Deep Learning and the recently obtained successes, topics that have been central to LCS for many years, such as human interpretability of the generated models, are now becoming of high interest to other machine learning communities (“Explainable AI”). Hence, this workshop serves as a critical spotlight to disseminate the long experience of LCS in these areas, to attract new interest, and expose the machine learning community to an alternate advantageous modeling paradigm.

Topics of interests include but are not limited to:
- New approaches for classifier modeling (e.g. ANN, GP, SVM, RBFs,…)
- New means for the partitioning of the problem space (condition structures, ensemble formation, …)
- New ways of classifier mixing (combination of local predictions, ensemble voting schemes,…)
- Evolutionary Reinforcement Learning (Multi-step LCS, Neuroevolution, …)
- Theoretical developments in LCS (provably optimal parametrization, scalability and learning bounds, ...)
- Flexibility of LCS systems regarding types of target problems (single-step/multiple-step reinforcement learning, regression/function approximation, classification, ...)
- Interpretability of evolved knowledge bases (knowledge extraction techniques, visualization approaches such as Attribute Tracking or Feature Dependency Trees …)
- System enhancements (competent operators, problem structure identification and linkage learning, ...)
- Input encoding / representations (binary, real-valued, oblique, non-linear, fuzzy, ...)
- Paradigms of LCS (Michigan, Pittsburgh, ...)
- LCS for Cognitive Control (architectures, emergent behaviors, ...)
- Applications (data mining, medical domains, bio-informatics, intelligence in games, ...)
- Optimizations and parallel implementations (GPU acceleration, matching algorithms,…)
- Similar Evolutionary Rule-Based ML systems (Artificial Immune Systems, Evolving FRI Systems, …)

Biographies

Anthony Stein

Anthony Stein is a postdoctoral research associate with the Institute of Computer Science at the University of Augsburg, Germany. He received his bachelor's degree (B.Sc.) in Business Information Systems from the University of Applied Sciences Augsburg in 2012. He then moved on to the University of Augsburg for his master's degree (M.Sc.) in computer science with a minor in information economics which he received in 2014. He holds a doctorate (Dr. rer. nat.) in computer science since 2019. His research is concerned with the application of AI methodology and evolutionary machine learning algorithms to complex self-adaptive and self-organizing (SASO) systems. Dr. Stein is involved in the organization of workshops on intelligent systems and evolutionary machine learning. He serves as reviewer for international conferences and journals, including ACM GECCO or IEEE T-EVC.

Masaya Nakata

Masaya Nakata eceived the B.A. and M.Sc. degrees in informatics from the University of Electro- Communications, Chofu, Tokyo, Japan, in 2011 and 2013 respectively. He is the Ph.D. candidate in the University of Electro- Communications, the research fellow of Japan Society for the Promotion of Science, Chiyoda-ku, Tokyo, Japan, and a visiting student of the School of Engineering and Computer Science in Victoria University of Wellington from 2014. He was a visiting student of the Department of Electronics and Information, Politecnico di Milano, Milan, Italy, in 2013, and of the Department of Computer Science, University of Bristol, Bristol, UK, in 2014. His research interests are in evolutionary computation, reinforcement learning, data mining, more specifically, in learning classifier systems. He has received the best paper award and the IEEE Computational Intelligence Society Japan Chapter Young Researcher Award from the Japanese Symposium of Evolutionary Computation 2012. He is a co-organizer of International Workshop on Learning Classifier Systems (IWLCS) for 2015-2016.

 

David Pätzel

David Pätzel is a PhD student at the Department of Computer Science at the University of Augsburg, Germany. He received his B. Sc. in Computer Science from the University of Augsburg in 2015 and his M. Sc. in the same field in 2017. His main research is directed towards Learning Classifier Systems, especially XCS and its derivatives, with a focus on developing a more formal understanding of LCS that can be used to improve existing algorithms by alleviating known weaknesses as well as discovering new ones. Besides that, his research interests include reinforcement learning, evolutionary machine learning algorithms and pure functional programming.

MedGEC — Medical Applications of Genetic and Evolutionary Computation

Summary

The Workshop focuses on the application of genetic and evolutionary computation (GEC) to problems in medicine and healthcare.
Subjects will include (but are not limited to) applications of GEC to:
• Medical imaging
• Medical signal processing
• Medical text analysis
• Medical publication mining
• Clinical diagnosis and therapy
• Data mining medical data and records
• Clinical expert systems
• Modelling and simulation of medical processes
• Drug description analysis
• Genomic-based clinical studies
• Patient-centric care

Although the application of GEC to medicine is not new, the reporting of new work tends to be distributed among various technical and clinical conferences in a somewhat disparate manner. A dedicated workshop at GECCO provides a much needed focus for medical related applications of EC, not only providing a clear definition of the state of the art, but also support to practitioners for whom GEC might not be their main area of expertise or experience.

Biographies

 

Neil Vaughan

Dr Neil Vaughan's research is on Evolution in Computational Healthcare. He is Research Fellow of the Royal Academy of Engineering (RFREng) and Senior Lecturer in Computer Science at University of Chester (UoC). He is a Senior Member of Artificial Intelligence and Simulation of Behaviour (AISB), Senior member of IEEE Computational Intelligence Society, Associate Fellow of AdvanceHE (HEA) and Editor of the Journal of Behavioural Robotics.

Stephen Smith

Stephen Smith is a full professor in the Department of Electronic Engineering at the University of York, UK. He received a BSc in Computer Science, an MSc in Electronics and a PhD in Electronic Engineering, all from the University of Kent, UK. Stephen's research uses genetic programming (a representation of Cartesian Genetic Programming) in the diagnosis and monitoring of Parkinson's disease, Alzheimer's disease and other neurodegenerative conditions. He has applied this work to clinical studies in the UK, USA, UAE, Australia, China and Singapore. The resulting technology is protected under 12 patent applications, of which 7 have been granted. A spinout company, ClearSky Medical Diagnostics (www.clearskymd.com), is marketing medical devices based on the technology and a recent co-authored publication detailing the clinical efficacy of this work won the Gold Humies award at GECCO 2018.

Stephen is co-founder and organizer of the MedGEC Workshop, which is now in its fourteenth year. He is also an associate editor for the journal Genetic Programming (Springer) and co-editor of a book on Medical Applications of Genetic and Evolutionary Computation (John Wiley, November 2010). Stephen has some 100 refereed publications, is a Chartered Engineer and a fellow of the British Computer Society.

Stefano Cagnoni

Stefano Cagnoni graduated in Electronic Engineering at the University of Florence, Italy, where he has been a PhD student and a post-doc until 1997. In 1994 he was a visiting scientist at the Whitaker College Biomedical Imaging and Computation Laboratory at the Massachusetts Institute of Technology. Since 1997 he has been with the University of Parma, where he has been Associate Professor since 2004.

Recent research grants include: co-management of a project funded by Italian Railway Network Society (RFI) aimed at developing an automatic inspection system for train pantographs; a "Marie Curie Initial Training Network" grant, for a four-year research training project in Medical Imaging using Bio-Inspired and Soft Computing; a grant from "Compagnia diS. Paolo" on "Bioinformatic and experimental dissection of the signalling pathways underlying dendritic spine function".

He has been Editor-in-chief of the "Journal of Artificial Evolution and Applications" from 2007 to 2010. Since 1999, he has been chair of EvoIASP, an event dedicated to evolutionary computation for image analysis and signal processing, now a track of the EvoApplications conference. Since 2005, he has co-chaired MedGEC, workshop on medical applications of evolutionary computation at GECCO. Co-editor of special issues of journals dedicated to Evolutionary Computation for Image Analysis and Signal Processing. Member of the Editorial Board of the journals “Evolutionary Computation” and “Genetic Programming and Evolvable Machines”.

He has been awarded the "Evostar 2009 Award", in recognition of the most outstanding contribution to Evolutionary Computation.

Robert M. Patton

Dr. Patton received his Ph.D. in Computer Engineering with emphasis on Software Engineering from the University of Central Florida in 2002. In 2003, he joined the Applied Software Engineering Research group of Oak Ridge National Laboratory as a researcher. Dr. Patton primary research interests include data and event analytics, intelligent agents, computational intelligence, and nature-inspired computing. He currently is investigating novel approaches of evolutionary computation to the analysis of mammograms, abdominal aortic aneurysms, and traumatic brain injuries. In 2005, he served as a member of the organizing committee for the workshop on Ambient Intelligence - Agents for Ubiquitous Environments in conjunction with the 2005 Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005).

NEvo@Work — Neuroevolution at work

Summary

In the last years, inspired by the fact that natural brains themselves are the products of an evolutionary process, the quest for evolving and optimizing artificial neural networks through evolutionary computation has enabled researchers to successfully apply neuroevolution to many domains such as strategy games, robotics, big data, and so on. The reason behind this success lies in important capabilities that are typically unavailable to traditional approaches, including evolving neural network building blocks, hyperparameters, architectures and even the algorithms for learning themselves (meta-learning).
Although promising, the use of neuroevolution poses important problems and challenges for its future developments.
Firstly, many of its paradigms suffer from lack of parameter-space diversity, meaning with this a failure in providing diversity in the behaviors generated by the different networks.
Moreover, the harnessing of neuroevolution to optimize deep neural networks requires noticeable computational power and, consequently, the investigation of new trends in enhancing the computational performance.

This workshop aims:
- to bring together researchers working in the fields of deep learning, evolutionary computation and optimization to exchange new ideas about potential directions for future research;
- to create a forum of excellence on neuroevolution that will help interested researchers from a variety of different areas, ranging from computer scientists and engineers on the one hand to application-devoted researchers on the other hand, to gain a high-level view about the current state of the art.

Since an increasing trend to neuroevolution in the next years seems likely to be observed, not only will a workshop on this topic be of immediate relevance to get in insight in future trends, it will also provide a common ground to encourage novel paradigms and applications. Therefore, researchers putting emphasis on neuroevolution issues in their work are encouraged to submit their work. This event is also the ideal place for informal contacts, exchanges of ideas and discussions with fellow researchers.

The scope of the workshop is to receive high-quality contributions on topics related to neuroevolution, ranging from theoretical works to innovative applications in the context of (but not limited to):
- theoretical and experimental studies involving neuroevolution on machine learning in general, and on deep and reinforcement learning in particular
- development of innovative neuroevolution paradigms
- parallel and distributed neuroevolution methods
- new search operators for neuroevolution
- hybrid methods for neuroevolution
- surrogate models for fitness estimation in neuroevolution
- applications of neuroevolution to Artificial Intelligence agents and to real-world problems

Biographies

Ivanoe De Falco

Ivanoe De Falco received his Laurea degree in Electrical Engineering cum laude in 1987 at the University of Naples “Federico II.”, and is currently a senior researcher at the Institute for High-Performance Computing and Networking (ICAR) of the National Research Council of Italy (CNR). His main research fields include computational intelligence and parallel computing. He serves as an Associate Editor for the Applied Soft Computing journal (Elsevier), is a member of the World Federation on Soft Computing (WFSC), has been part of the Organizing or Scientific Committees for tens of international conferences or workshops, and has authored or coauthored about 120 papers in international journals, books, and conference proceedings.

Antonio Della Cioppa

Antonio Della Cioppa received the Laurea degree in physics and the Ph.D. degree in computer science, both from University of Naples “Federico II,” Naples, Italy, in 1993 and 1999, respectively.

From 1999 to 2003, he was a Postdoctoral Fellow at the Department of Computer Science and Electrical Engineering, University of Salerno, Salerno, Italy. In 2004, he joined the Department of Electrical and Information Engineering, University of Salerno, where he is currently Associate Professor of Computer Science and Artificial Intelligence. He is active in the fields of Artificial Intelligence and Cybernetics. His current research interests are in the fields of theoretical and computational physics (complexity, statistical mechanics of equilibrium and nonequilibrium phenomena, theory of dynamical systems, chaos), prebiotic evolution, Darwinian dynamics and speciation, evolutionary computation, and artificial life.

Dr. Della Cioppa is a member of the Association for Computing Machinery (ACM), the IEEE Computer Society, the IEEE Computational Intelligence Society. He serves as referee for many relevant international journals. He is also member of the program committee of many relevant international conferences such as the Genetic and Evolutionary Computation Conference and Conference on Evolutionary Computation.

Umberto Scafuri

Umberto Scafuri was born in Baiano (AV) on May 21, 1957. He got his Laurea degree in Electrical Engineering at the University of Naples "Federico II" in 1985. He currently works as a technologist at the Institute of High Performance Computing and Networking (ICAR) of the National Research Council of Italy (CNR). His research activity is basically devoted to parallel and distributed architectures and evolutionary models.

Ernesto Tarantino

Ernesto Tarantino was born in S. Angelo a Cupolo, Italy, in 1961. He received the Laurea degree in Electrical Engineering in 1988 from University of Naples, Italy. He is currently a researcher at National Research Council of Italy. After completing his studies, he conducted research in parallel and distributed computing. During the past decade his research interests have been in the fields of theory and application of evolutionary techniques and related areas of computational intelligence. He is author of numerous scientific papers in international conferences and journals. He has served on several program committees of conferences in the area of evolutionary computation.

PDEIM — Parallel and Distributed Evolutionary Inspired Methods

Summary

Nature inspired methods include all paradigms of evolutionary computation such as genetic algorithms, evolution strategies,
genetic programming, ant algorithms, particle swarm systems and so on. These methods are being more and more frequently used
to face real-world problems characterized by a huge number of possible solutions, thus their execution often requires large amounts of time. Therefore, they can highly benefit from parallel and distributed implementations, in terms of both reduction in execution time and improvement in quality of the achieved solutions.

The workshop aims at creating a forum of excellence on the use of parallel models of evolutionary computation methods. This can be achieved by bringing together for an exchange of ideas researchers from a variety of different areas, ranging from computer scientists and engineers on the one hand to application-devoted researchers like biologists, chemists, physicians on the other hand.

Since we are going to increasingly observe a trend towards parallelization of evolutionary models in the next years, not only will a Workshop on this topic be of immediate relevance, it will also provide a platform for encouraging such implementations.

Researchers putting emphasis on parallel issues in their work with evolutionary systems are encouraged to submit their work. This event is the ideal place for informal contact, exchange of ideas and discussions with fellow researchers.

The scope of the workshop is to receive high-quality contributions on topics related to parallel and distributed versions of evolutionary methods, ranging from theoretical work to innovative applications in the context of (but not limited to):

1. Theoretical and experimental studies on parallel and distributed model implementations (population size, synchronization, homogeneity, communication, topology, speedup, etc.)
2. New trends in parallel and distributed evolutionary computation including Grid and Cloud Computing, Internet Computing, General Purpose Computation on Graphics Processing Units (GPGPU), multi-core architectures and supercomputers.
3. New parallel and distributed evolutionary models.
4. Parallel and distributed implementation of evolutionary-fuzzy, evolutionary-neuro and evolutionary-neuro-fuzzy hybrids.
5. Parallel and distributed evolutionary algorithms for data
mining on big data and machine learning.
6. Parallel and distributed evolutionary deep learning.
7. Parallel and distributed multi-objective evolutionary algorithms.
8. Real-world applications of parallel and distributed evolutionary algorithms.

Biographies

Ernesto Tarantino

Ernesto Tarantino was born in S. Angelo a Cupolo, Italy, in 1961. He received the Laurea degree in Electrical Engineering in 1988 from University of Naples, Italy. He is currently a researcher at National Research Council of Italy. After completing his studies, he conducted research in parallel and distributed computing. During the past decade his research interests have been in the fields of theory and application of evolutionary techniques and related areas of computational intelligence. He is author of numerous scientific papers in international conferences and journals. He has served on several program committees of conferences in the area of evolutionary computation.

Ivanoe De Falco

Ivanoe De Falco received his Laurea degree in Electrical Engineering cum laude in 1987 at the University of Naples “Federico II.”, and is currently a senior researcher at the Institute for High-Performance Computing and Networking (ICAR) of the National Research Council of Italy (CNR). His main research fields include computational intelligence and parallel computing. He serves as an Associate Editor for the Applied Soft Computing journal (Elsevier), is a member of the World Federation on Soft Computing (WFSC), has been part of the Organizing or Scientific Committees for tens of international conferences or workshops, and has authored or coauthored about 120 papers in international journals, books, and conference proceedings.

Antonio Della Cioppa

Antonio Della Cioppa received the Laurea degree in physics and the Ph.D. degree in computer science, both from University of Naples “Federico II,” Naples, Italy, in 1993 and 1999, respectively.

From 1999 to 2003, he was a Postdoctoral Fellow at the Department of Computer Science and Electrical Engineering, University of Salerno, Salerno, Italy. In 2004, he joined the Department of Electrical and Information Engineering, University of Salerno, where he is currently Associate Professor of Computer Science and Artificial Intelligence. He is active in the fields of Artificial Intelligence and Cybernetics. His current research interests are in the fields of theoretical and computational physics (complexity, statistical mechanics of equilibrium and nonequilibrium phenomena, theory of dynamical systems, chaos), prebiotic evolution, Darwinian dynamics and speciation, evolutionary computation, and artificial life.

Dr. Della Cioppa is a member of the Association for Computing Machinery (ACM), the IEEE Computer Society, the IEEE Computational Intelligence Society. He serves as referee for many relevant international journals. He is also member of the program committee of many relevant international conferences such as the Genetic and Evolutionary Computation Conference and Conference on Evolutionary Computation.

Umberto Scafuri

Umberto Scafuri was born in Baiano (AV) on May 21, 1957. He got his Laurea degree in Electrical Engineering at the University of Naples "Federico II" in 1985. He currently works as a technologist at the Institute of High Performance Computing and Networking (ICAR) of the National Research Council of Italy (CNR). His research activity is basically devoted to parallel and distributed architectures and evolutionary models.

RWACMO — Real-World Applications of Continuous and Mixed-Integer Optimization

Summary

Continuous and mixed-integer optimization are two fields where evolutionary computation (EC) and related techniques (e.g. particle swarm optimization and differential evolution) have been successfully applied in disciplines such as engineering design, robotics, and bioinformatics. Real-world continuous and mixed-integer problems possess unique challenges that cannot be fully replicated by algebraic and artificial problems, where characteristics of these problems could be different across a variety of scientific fields. Some of these characteristics are expensive function evaluations, huge design spaces, multi/many-objective optimization, correlated variables, etc. Besides optimization, EC/related techniques also frequently work hand-in-hand with machine learning and data mining tools to explore trade-off and to infer important knowledge that is highly useful for real-world optimization processes. Fundamental differences between combinatorial and continuous/mixed integer optimization lead to different approaches in the research, algorithmic development, and applications of EC/related techniques. It is important that a special focus needs to be given on real-world applications to synergize the research in EC/related techniques with real-world applications in both industry and academia, which, in turn, will also benefit research in algorithmic development.

This workshop aims to act as a medium for debate, exchange of knowledge and experience, and encourage collaboration for researchers and practitioners from a range of disciplines to discuss the recent challenges and applications of EC/related techniques for solving real-world continuous and mixed-integer optimization problems. The workshop will feature: 1) two invited talks from researchers/practitioners with a successful record on applications of EC for solving continuous/mixed integer problems, 2) presentations of submission-based papers, and 3) final discussion with the speakers and audiences to talk about future challenges. The workshop encourages submission from various disciplines to stimulate multidisciplinary research discussion. The invited speakers are expected to deliver talks with the following topics: 1) current advancements of EC/related techniques in handling real-world problems, 2) interaction and synergy between algorithmic development and real-world problem solving, or 3) successful application of EC/related techniques in boosting the productivity and efficiency in the industry.

The topics for the paper submission include:
- Real-world applications in a specific field either in academia or industry.
- Algorithmic development for solving real-world applications.
- Design exploration, data mining, machine learning and their synergy with EC/related techniques.
- Applications of multi- and many-objective EC/related techniques in real-world problems.
- Real-world optimization/design under the presence of uncertainties.
- Known issues and challenges in real-world implementations and how to tackle them.
- Review paper on the applications of EC/related techniques in a specific discipline.
- Competitiveness and disadvantages of EC/related techniques compared to other techniques such as gradient-based methods.
- Comparison and performance assessment of various EC/related techniques for solving real-world problems.

Biographies

Akira Oyama

Akira Oyama is an associate professor at Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency (JAXA) and the University of Tokyo in Japan. Previously, he worked for NASA Glenn research center in the U.S. from 2000 to 2003. His research interests include computational fluid dynamics and many/multi-objective design optimization in space engineering. He is the leader of "design innovation with multiobjective design exploration," one of the research topics of Japanese national supercomputer project "HPCI Strategic Programs for Innovative Research Field 4: Design Innovation" since 2010. He has published 265 conference papers and 33 refereed journal articles.

Koji Shimoyama

Koji Shimoyama is an associate professor in the Institute of Fluid Science, Tohoku University, Japan. He obtained his Ph.D. from the Department of Aeronautics and Astronautics, University of Tokyo, Japan, in 2006. Previously, he was a research assistant at JAXA, a research fellow at Tohoku University, a visiting scholar at Stanford University, United States, and an invited Professor at Ecole Centrale De Lyon, France. His research interests are multi-objective design exploration for engineering design, robust design optimization, and uncertainty quantification. He has performed collaborations with various industries in Japan regarding the application of EC and surrogate models for real-world product design and development.

Hemant Kumar Singh

Dr. Hemant Kumar Singh completed his Ph.D. from University of New South Wales (UNSW) Australia in 2011 and B.Tech in Mechanical Engineering from Indian Institute of Technology (IIT) Kanpur in 2007. Since 2013, he has worked with UNSW Australia as a Lecturer (2013-2017) and Senior Lecturer (2017-) in the School of Engineering and Information Technology. He also worked with GE Aviation at John F. Welch Technology Centre as a Lead Engineer during 2011-13. His research interests include development of evolutionary computation methods with a focus on engineering design optimization problems. He has over 50 refereed publications this area. He is the recipient of Australia Bicentennial Fellowship 2016, WCSMO Early Career Researcher Fellowship 2015 and The Australian Society for Defence Engineering Prize 2011 among others.

Kazuhisa Chiba

Kazuhisa Chiba is an associate professor in the graduate school of informatics and engineering, the University of Electro-Communications, Japan. Previously, he was a researcher at JAXA, a researcher at Mitsubishi Heavy Industries, and an associate professor at Hokkaido University of Science. His research interests are aerospace vehicles design via design informatics and multi/many-objective optimization.

Pramudita Satria Palar

Pramudita Satria Palar is a lecturer at Faculty of Mechanical and Aerospace Engineering, Bandung Institute of Technology, Indonesia. Previously, he was a research fellow at Tohoku University, Japan, from 2016-2018. He obtained his Ph.D. from the Department of Aeronautics and Astronautics, University of Tokyo, Japan, in 2015. During his doctoral study, he was also a visiting researcher at Engineering Design Center of University of Cambridge, United Kingdom, and wrote several collaborative papers with the Center. His research interests include aerodynamic design optimization, surrogate-assisted optimization, and uncertainty quantification. He has published several journal and conference papers on the development and application of evolutionary computations and surrogate models in the field of aerospace and biomedical engineering

SAEOpt — Workshop on Surrogate-Assisted Evolutionary Optimisation

Summary

In many real world optimisation problems evaluating the objective function(s) is expensive, perhaps requiring days of computation for a single evaluation. Surrogate-assisted optimisation attempts to alleviate this problem by employing computationally cheap 'surrogate' models to estimate the objective function(s) or the ranking relationships of the candidate solutions.

Surrogate-assisted approaches have been widely used across the field of evolutionary optimisation, including continuous and discrete variable problems, although little work has been done on combinatorial problems. Surrogates have been employed in solving a variety of optimization problems, such as multi-objective optimisation, dynamic optimisation, and robust optimisation. Surrogate-assisted methods have also found successful applications to aerodynamic design optimisation, structural design optimisation, data-driven optimisation, chip design, drug design, robotics and many more. Most interestingly, the need for on-line learning of the surrogates has led to a fruitful crossover between the machine learning and evolutionary optimisation communities, where advanced learning techniques such as ensemble learning, active learning, semi-supervised learning and transfer learning have been employed in surrogate construction.

Despite recent successes in using surrogate-assisted evolutionary optimisation, there remain many challenges. This workshop aims to promote the research on surrogate assisted evolutionary optimization including the synergies between evolutionary optimisation and learning. Thus, this workshop will be of interest to a wide range of GECCO participants. Particular topics of interest include (but are not limited to):

  • Advanced machine learning techniques for constructing surrogates
  • Model management in surrogate-assisted optimisation
  • Multi-level, multi-fidelity surrogates
  • Complexity and efficiency of surrogate-assisted methods
  • Small and big data driven evolutionary optimization
  • Model approximation in dynamic, robust and multi-modal optimisation
  • Model approximation in multi- and many-objective optimisation
  • Surrogate-assisted evolutionary optimisation of high-dimensional problems
  • Comparison of different modelling methods in surrogate construction
  • Surrogate-assisted identification of the feasible region
  • Comparison of evolutionary and non-evolutionary approaches with surrogate models
  • Test problems for surrogate-assisted evolutionary optimisation
  • Performance improvement techniques in surrogate-assisted evolutionary computation
  • Performance assessment of surrogate-assisted evolutionary algorithms

Biographies

Alma Rahat

Alma Rahat is a Lecturer in Computer Science at the University of Plymouth. He has a BEng in Electronic Engineering from the University of Southampton, and a PhD in Computer Science from the University of Exeter. He has been a product development engineer before starting his PhD, and has held post-doctoral research positions at the University of Exeter before starting his role in Plymouth. His research interests lie in fast hybrid optimisation methods, real-world problems and machine learning. Current research is on the use of surrogate-assisted optimisation approaches for computationally expensive problems.

Richard Everson

Richard Everson is Professor of Machine Learning at the University of Exeter. He has a degree in Physics from Cambridge University and a PhD in Applied Mathematics from Leeds University. He worked at Brown and Yale Universities on fluid mechanics and data analysis problems until moving to Rockefeller University, New York, to work on optical imaging and modelling of the visual cortex. After working at Imperial College, London, he joined the Computer Science department at Exeter University.

His research interests lie in statistical pattern recognition, multi-objective optimisation and the links between them. Recent interests include the optimisation of the performance of classifiers, which can be viewed as a many-objective optimisation problem requiring novel methods for visualisation. Research on the construction of league tables has led to publications exploring the multi-objective nature and methods of visualising league tables. Current research is on surrogate methods for large optimisation problems, particularly computational fluid dynamics design optimisation.

Jonathan Fieldsend

Jonathan Fieldsend is an Associate Professor in Computational Intelligence at the University of Exeter. He has a degree in Economics from Durham University, a Masters in Computational Intelligence from the University of Plymouth and a PhD in Computer Science from the University of Exeter. He has held postdoctoral positions as a Research Fellow (working on the interface of Bayesian modelling and optimisation) and as a Business Fellow (focusing on knowledge transfer to industry) prior to his appointment to an academic position at Exeter.

He has published widely on theoretical and applied aspects of evolutionary multi-objective optimisation, and also in the field of machine learning — and has ongoing interests on the interface between these areas. His theoretical work includes algorithm development and analysis, along with data structures required for efficient multi-objective optimisation and Pareto set maintenance. His applied work includes costly and uncertain industrial design problems, air traffic control safety systems, automating biological experiments and robust multi-objective routing.

He has previous been a workshop organiser at GECCO for VizGEC (Visualisation Methods in Genetic and Evolutionary Computation), SAEOpt (Surrogate-Assisted Evolutionary Optimisation) and EAPU (Evolutionary Algorithms for Problems with Uncertainty). He has been active within the evolutionary computation community as a reviewer and program committee member since 2003.

Handing Wang

1. Handing Wang received the B.Eng. and Ph.D. degrees from Xidian University, Xi'an, China, in 2010 and 2015. She is currently a research follow with the Department of Computer Science, University of Surrey, Guildford, UK. Her research interests include nature-inspired computation, multi- and many-objective optimization, multiple criteria decision making, and real-world problems. She has published over 10 papers in international journal, including IEEE Transactions on Evolutionary Computation (TEVC), IEEE Transactions on Cybernetics (TCYB), and Evolutionary Computation (ECJ).

Yaochu Jin

Yaochu Jin received the B.Sc., M.Sc., and PhD degrees from Zhejiang University, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. Degree from Ruhr University Bochum, Germany, in 2001. He is currently a Professor in Computational Intelligence and Head of the Nature Inspired Computing and Engineering (NICE) Group, Department of Computer Science, University of Surrey, UK. He was a Finland Distinguished Professor and Changjiang Distinguished Professor. His research interests include data-driven evolutionary optimization, Bayesian optimization, secure and interpretable machine learning, evolutionary multi-objective learning, evolutionary developmental systems, and neural plasticity.

He is the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems and the Co-Editor-in-Chief of Complex & Intelligent Systems. He is also an Associate Editor of the IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, and IEEE Transactions on NanoBioscience. He is an Editorial Board Member of Evolutionary Computation. He is an Invited Plenary / Keynote Speaker at over 30 international conferences. He is the recipient of several awards including the 2014 and 2016 IEEE Computational Intelligence Magazine Outstanding Paper Award, and the 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is an IEEE Distinguished Lecturer.

Dr Jin is a Fellow of IEEE.

SecDef 2020 — Genetic and Evolutionary Computation in Defense, Security, and Risk Management

https://projects.cs.dal.ca/projectx/secdef/

Summary

With the constant appearance of new threats, research in the areas of defense, security and risk management has acquired an increasing importance over the past few years. These new challenges often require innovative solutions and computational intelligence techniques can play a significant role in finding them.
In the last six years, we have been organizing the SecDef workshop under GECCO to seek both theoretical developments and applications of Genetic and Evolutionary Computation and their hybrids to the following (and other related) topics:
• Cyber-crime and cyber-defense: anomaly detection systems, attack prevention and defense, threat forecasting systems, anti-spam,
antivirus systems, cyber warfare, cyber fraud;
• IT Security: Intrusion detection, behavior monitoring, network
traffic analysis;
• Risk management: identification, prevention, monitoring and
handling of risks, risk impact and probability estimation systems,
contingency plans, real time risk management;
• Critical Infrastructure Protection (CIP);
• Military, counter-terrorism and other defense-related aspects.
The workshop invites both completed and ongoing work, with the aim to encourage communication between active researchers and practitioners to better understand the current scope of efforts within this domain. The ultimate goal is to understand, discuss, and help set future directions for computational intelligence in security and defense problems.

Biographies

 

Erik Hemberg

 

Riyad Alshammari

Riyad Alshammari is an Associate Professor in Computer Science and Joint-Associate Professor in Health Informatics at the Department of Health Informatics at the College of Public Health and Health Informatics, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia. Dr. Alshammari is specialized in Network security, Network analysis and forensic, and machine learning. Dr. Alshammari's research interest include but not limited to the areas of Data mining, Machine Learning, Classification, Clinical Informatics, e- Health, Computer Network and Homeland Security. Dr. Alshammari is the Chairman of the Health Informatics Department and Director of Center of Excellence in Health Informatics, College of Public Health and Health Informatics, King Saud Bin Abdulaziz University For Health Sciences, Riyadh, Saudi Arabia. Dr. Alshammari has been elected as the President of Saudi Association for Health Informatics (SAHI).

 

Tokunbo Makanju

Tokunbo Makanju is Research Engineer with the Cybersecurity Laboratory at KDDI Research, Fuijimino-shi, Japan. His research interests are at the intersection of Big Data Analytics, Machine Learning, Network Management and Cybersecurity. Dr. Makanju is a member of the IEEE and the ACM.

SWINGA — Swarm Intelligence Algorithms: Foundations, Perspectives and Challenges

Summary

Evolutionary computation, as well as complex systems dynamics and structure, is a vibrant area of research since the last few decades. To date, a large set of modern and novel techniques are created and used. Such algorithms, systems and their mutual fusion form an inevitable part of computational science and engineering. Most notable examples include not only algorithms inspired by behaviour from biological realm but also chaos control and synchronization, chaotic dynamics for pseudo-random number generators in evolutionary algorithms, use of chaos game with evolutionary algorithms and use of evolution in complex systems design and analysis.
This workshop is concerned about the swarm intelligence algorithm, as the prominent algorithms like Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Firefly, and so on. Also, new promising algorithms like Self-Organizing Migrating Algorithm (SOMA), based on competitive-cooperative phases, inherent self-adaptation of movement over the search space, as well as by discrete perturbation mimicking the mutation process known from the classical evolutionary computing techniques will be included. Those algorithms perform well and outperform significantly many classical ones well in both continuous as well as discrete domains. The swarm algorithms have been used successfully on various tasks as the real-time plasma reactor control, aircraft wings optimization, chaos control, large scale, combinatorial and permutative optimisation tasks.
This workshop is concern about original research papers discussing new results, as well as it's novel improvements tested on widely accepted benchmark tests. This workshop aims to bring together people from fundamental research, experts from various applications to develop mutual intersections and fusion. Also, a discussion of possible hybridization amongst them as well as real-life experiences with computer applications will be carried out to define new open problems in this interesting and fast-growing field of research. The workshop will focus on, but not limited to, the following topics:

• The theoretical aspect of the swarm intelligence
• Swarm intelligence hybridisation with other metaheuristics
• The performance improvement, testing and efficiency of the swarm intelligence
• Swarm intelligence for complex optimisation scenarios:
• constrained optimisation
• multiobjective optimisation
• many-objective optimisation
• multimodal optimisation and niching
• expensive and surrogate assisted optimisation
• dynamic and uncertain optimisation
• large-scale optimisation
• Swarm intelligence and its parallelisation
• Swarm intelligence for discrete optimisation
• Randomness, chaos and its impact on the swarm intelligence dynamics and algorithm performance.
• Swarm intelligence in real-world applications
• And more…

Biographies

Ivan Zelinka

Ivan Zelinka is currently working at the Technical University of Ostrava (VSB-TU), Faculty of Electrical Engineering and Computer Science. He graduated consequently at Technical University in Brno (1995 – MSc.), UTB in Zlin (2001 – PhD) and again at Technical University in Brno (2004 – assoc. prof.) and VSB-TU (2010 - professor). Before academic career, he was an employed like TELECOM technician, computer specialist (HW+SW) and Commercial Bank (computer and LAN supervisor).
During his career at UTB, he proposed and opened 7 different lectures. He also has been invited for lectures at numerous universities in different EU countries plus the role of the keynote speaker at the Global Conference on Power, Control and Optimization in Bali, Indonesia (2009), Interdisciplinary Symposium on Complex Systems (2011), Halkidiki, Greece and IWCFTA 2012, Dalian China. ICAISC Poland, INTELS Russia. The field of his expertise if mainly on unconventional algorithms and cybersecurity.
He is and was the responsible supervisor of 3 grant of fundamental research of Czech grant agency GAČR, co-supervisor of grant FRVŠ - Laboratory of parallel computing. He was also working on numerous grants and two EU project like a member of the team (FP5 - RESTORM) and supervisor (FP7 - PROMOEVO) of the Czech team and supervisor of international research (founded by TACR agency) focused on the security of mobile devices (Czech - Vietnam).
Currently, he is a professor at the Department of Computer Science and in total, he has been the supervisor of more than 40 MSc. and 25 Bc. diploma thesis. Ivan Zelinka is also supervisor of doctoral students including students from the abroad.
He was awarded by Siemens Award for his PhD thesis, as well as by journal Software news for his book about artificial intelligence. Ivan Zelinka is a member of British Computer Society, Editor in chief of Springer book series: Emergence, Complexity and Computation (http://www.springer.com/series/10624), Editorial board of Saint Petersburg State University Studies in Mathematics, a few international program committees of various conferences and international journals. He is the author of journal articles as well as of books in Czech and English language and one of three founders of TC IEEE on big data http://ieeesmc.org/about-smcs/history/2014-archives/44-about-smcs/history/2014/technical-committees/204-big-data-computing/ . He is also head of research group NAVY http://navy.cs.vsb.cz.

 

Swagatam Das

Swagatam Das received the B. E. Tel. E., M. E. Tel. E (Control Engineering specialization) and Ph. D. degrees, all from Jadavpur University, India, in 2003, 2005, and 2009 respectively. Swagatam Das is currently serving as an associate professor at the Electronics and Communication Sciences Unit of the Indian Statistical Institute, Kolkata, India. His research interests include evolutionary computing, deep learning and non convex optimization in general. Dr. Das has published more than 300 research articles in peer-reviewed journals and international conferences. He is the founding co-editor-in-chief of Swarm and Evolutionary Computation, an international journal from Elsevier. He has also served as or is serving as the associate editors of the IEEE Trans. on Systems, Man, and Cybernetics: Systems, IEEE Computational Intelligence Magazine, Pattern Recognition (Elsevier),Neurocomputing (Elsevier),Engineering Applications of Artificial Intelligence (Elsevier), and Information Sciences (Elsevier). He is a founding Section Editor of Springer Nature Computer Science journal since 2019. Dr. Das has 18000+ Google Scholar citations and an H-index of 63 till date. He has been associated with the international program committees of several regular international conferences including IEEE CEC, IEEE SSCI, SEAL, GECCO, AAAI, and SEMCCO. He has acted as guest editors for special issues in journals like IEEE Transactions on Evolutionary Computation and IEEE Transactions on SMC, Part C. He is the recipient of the 2012 Young Engineer Award from the Indian National Academy of Engineering (INAE). He is also the recipient of the 2015 Thomson Reuters Research Excellence India Citation Award as the highest cited researcher from India in Engineering and Computer Science category between 2010 to 2014.

Ponnuthurai Nagaratnam Suganthan

Ponnuthurai Nagaratnam Suganthan received the B.A degree, Postgraduate Certificate and M.A degree in Electrical and Information Engineering from the University of Cambridge, UK in 1990, 1992 and 1994, respectively. After completing his PhD research in 1995, he served as a pre-doctoral Research Assistant in the Dept. of Electrical Engineering, University of Sydney in 1995–96 and a lecturer in the Dept. of Computer Science and Electrical Engineering, University of Queensland in 1996–99. He moved to Singapore in 1999. He was an Editorial Board Member of the Evolutionary Computation Journal, MIT Press (2013-2018) and an associate editor of the IEEE Trans on Cybernetics (2012 - 2018). He is an associate editor of Applied Soft Computing (Elsevier, 2018-), Neurocomputing (Elsevier, 2018-), IEEE Trans on Evolutionary Computation (2005 -), Information Sciences (Elsevier, 2009 - ), Pattern Recognition (Elsevier, 2001 - ) and Int. J. of Swarm Intelligence Research (2009 - ) Journals. He is a founding co-editor-in-chief of Swarm and Evolutionary Computation (2010 - ), an SCI Indexed Elsevier Journal. His co-authored SaDE paper (published in April 2009) won the ""IEEE Trans. on Evolutionary Computation outstanding paper award"" in 2012.

 

Roman Senkerik

VizGEC 2020 — Visualisation Methods in Genetic and Evolutionary Computation

Summary

Building on workshops held annually since 2010, the eighth annual workshop on Visualisation Methods in Genetic and Evolutionary Computation (VizGEC), to be held at GECCO 2020 in Cancun, aims to explore, evaluate and promote current visualisation developments in the area of genetic and evolutionary computation (GEC). Visualisation is a crucial tool in this area, providing vital insight and understanding into algorithm operation and problem landscapes as well as enabling the use of GEC methods on data science tasks. Particular topics of interest are:

* visualisation of the evolution of a synthetic genetic population
* visualisation of algorithm operation
* visualisation of problem landscapes
* visualisation of multi-objective trade-off surfaces and Pareto fronts
* the use of genetic and evolutionary techniques for visualising data
* novel technologies for visualisation within genetic and evolutionary computation
* visualisation for interactive algorithms
* non-visual techniques for presenting results (e.g. audio and audio-visual)

As well as allowing us to observe how individuals interact, visualising the evolution of a synthetic genetic population over time facilitates the analysis of how individuals change during evolution, permitting observation and interception of undesirable traits such as premature convergence and population stagnation. In addition, by visualising the problem landscape we can explore the distribution of solutions generated with a GEC method to ensure that the landscape has been fully explored. In the case of multi- and many-objective optimisation problems this is enhanced by the visualisation of the trade-off between objectives, a non-trivial task for problems comprising four or more objectives, where it is necessary to provide an intuitive visualisation of the Pareto front to a human decision maker. All of these areas are drawn together in the field of interactive evolutionary computation, where decision makers need to be provided with as much information as possible since they are required to interact with the GEC method in an efficient manner, in order to generate and understand good solutions quickly.

In addition to visualising the solutions generated by a GEC process, we can also visualise the processes themselves. It can be useful, for example, to investigate which evolutionary operators are most commonly applied by an algorithm, as well as how they are applied, in order to gain an understanding of how the process can be most effectively tuned to solve the problem at hand. Advances in animation and the prevalence of digital display, rather than relying on the paper-based presentation of a visualisation, mean that it is possible to use visualisation methods so that aspects of an algorithm's performance can be evaluated online in real time.

GEC methods have also recently been applied to the visualisation of data. As the amount of data available to data scientists increases rapidly, it is necessary to develop methods that can visualise large quantities of data; evolutionary methods can, and have, been used for this. Work on visualising the results of evolutionary data mining is also now appearing.

As well as presenting the results of a GEC process in a traditional visual way, we are also keen to solicit work on other forms of presentation, such as audio.

Based on these areas of interest the target audience for VizGEC is broad. We anticipate that people engaged in visualisation research will be interested, in addition to people from the GEC community who may be interested in using visualisation to advance their own work. We hope to attract both experienced practitioners as well as providing an introduction for those new to visualisation in GEC. We intend to solicit novel visualisation work through the submission of papers, and will also encourage the demonstration of recently published visualisation methods during the workshop.

Biographies

 

David Walker

Lecturer in Computer Science
University of Plymouth

Richard Everson

Richard Everson is Professor of Machine Learning at the University of Exeter. He has a degree in Physics from Cambridge University and a PhD in Applied Mathematics from Leeds University. He worked at Brown and Yale Universities on fluid mechanics and data analysis problems until moving to Rockefeller University, New York, to work on optical imaging and modelling of the visual cortex. After working at Imperial College, London, he joined the Computer Science department at Exeter University.

His research interests lie in statistical pattern recognition, multi-objective optimisation and the links between them. Recent interests include the optimisation of the performance of classifiers, which can be viewed as a many-objective optimisation problem requiring novel methods for visualisation. Research on the construction of league tables has led to publications exploring the multi-objective nature and methods of visualising league tables. Current research is on surrogate methods for large optimisation problems, particularly computational fluid dynamics design optimisation.

 

Rui Wang

 

Neil Vaughan

Dr Neil Vaughan's research is on Evolution in Computational Healthcare. He is Research Fellow of the Royal Academy of Engineering (RFREng) and Senior Lecturer in Computer Science at University of Chester (UoC). He is a Senior Member of Artificial Intelligence and Simulation of Behaviour (AISB), Senior member of IEEE Computational Intelligence Society, Associate Fellow of AdvanceHE (HEA) and Editor of the Journal of Behavioural Robotics.